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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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PANX2 and brain lower grade glioma genesis: A bioinformatic analysis

引用

Science Progress 2021年第2期104卷 368504211011836页

作者： Xu, XiaoXue Hao, YueHan Xiong, Shuang He, ZhiYi Department of Neurology The First Hospital of China Medical University Shenyang China Key Laboratory of Neurological Disease Big Data of Liaoning Province Shenyang China Liaoning Academy of Analytic Science Construction Engineering Center of Important Technology Innovation and Research and Development Base in Liaoning Province Shenyang China

PANX2 forms large-pore channels mediating ATP release in response to physiological and pathological stimuli. Although PANX2 shows involvements in glioma genesis, the underlying mechanism remains unclear. PANX2 mRNA expression was analyzed via Oncomine and was confirmed via Gene Expression Profiling Interactive Analysis (GEPIA). The influence of PANX2 on overall survival (OS) of glioma was evaluated using LinkedOmics and further assessed through Cox regression analysis. The correlated genes with PANX2 acquired from LinkedOmics were validated through GEPIA and cBioPortal. Protein-protein interaction (PPI) of these genes was then obtained using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with MCODE plug-in. All the PANX2-related genes underwent Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The correlation between PANX2 and cancer immune infiltrates was evaluated via Tumor Immune Estimation Resource (TIMER). A higher expression of PANX2 only revealed a better OS in brain low grade glioma (LGG). PANX2-related genes in LGG functionally enriched in neuroactive ligand-receptor interaction, synaptic vesicle cycle, and calcium signaling. The hub genes from highest module of PPI were mainly linked to chemical synaptic transmission, plasma membrane, neuropeptide, and the pathway of neuroactive ligand-receptor interaction. Besides, PANX2 expression was negatively associated with infiltrating levels of macrophage, dendritic cells, and CD4+ T cells. This study demonstrated that PANX2 likely participated in LGG pathogenesis by affecting multiple molecular pathways and immune-related processes. PANX2 was associated with LGG prognosis and might become a promising therapeutic target of LGG. © The Author(s) 2021.

关键词： Brain Lower Grade Gliomas Immune Infiltrates Microenvironment PANX2 Tumorigenesis

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A Novel L1 Gain Performance Based Multi-robot System Formulation Control Design Method

A Novel L1 Gain Performance Based Multi-robot System Formula...

引用

第32届中国控制与决策会议

作者： Xiongjun Wu Jialing Zhou Dequan Li Hongbo Zhao National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering The 802 Institute of Shanghai Academy of Space Flight TechnologyThe Eighth Academy of China Aerospace Science and Technology Corporation School of Automation Nanjing University of Science and Technology School of Mathematics and Big Data Anhui University of Science and Technology School of Automation Science and Electrical Engineering Beihang University

The time-varying formations are usually required in practice for multi-agent systems to track the trajectories generated by multiple *** ability to maintain a stable formation property is helpful in terms of facilitating subsequent tasks such as collaborative detection,cooperative reconnaissance and performing other specific task like fetch and transport goods together as a ***,most of existing methods,on the other hand,are fragile to the measurement noises and network ***,it seems that few results are available that considering the internal and external control loop to arrive the consensus of position,the velocity and the attitude *** overcome these limitations,in this paper,we proposed a novel scheme and developed a novel L gain algorithm,where the integrated two loop robot control scheme is endowed with disturbance rejection ability,and it can maintain the formation structure during motion along a pre-determined or online generated geometric path,and to follow a timing law that dominates the rate of advancement of the *** conditions are established to guarantee the stability,the invariant set property,the L gain index,and also the initial tracking error bounding constraints,which are formulated and presented in terms of LMIs/BMIs and can be readily solved to obtain the *** simulations are carried out to validate the effectiveness of the proposed method,and the five robots as well as the three robots formulation cases are detail *** turns out that,in addition to providing a novel perspective of the formulation control problem with disturbance rejection ability,the approach adopted in this paper also paves the way to several extensions in relation to control of multi-agent systems in accordance with swarm intelligence principles,such as the collision avoidance,the delayed multi-agent system control,the incorporation of multiple simultaneous objectives and control design under communi

关键词： Formation Control L1 Gain Multi-agent Systems Disturbance Rejection Cooperative Detection

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Weighted Feature Pyramid Networks for Object Detection

Weighted Feature Pyramid Networks for Object Detection

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Xiaohan Li Taotao Lai Shuaiyu Wang Quan Chen Changcai Yang Riqing Chen Jinxun Lin Fu Zheng Digital Fujian Institute of Big Data for Agriculture and Forestry College of Computer and information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fu Jian Shu Bo Xun Information Technology Co. Ltd

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and semantic information in the object detection. Thus, FPN and its variants cannot detect some objects on challenging images. In this paper, based on FPN, we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection. The proposed method, is called a Weighted Feature Pyramid Network (WFPN), and shows significant improvement over the traditional feature pyramids in several applications. Using WFPN in Faster R-CNN system, the proposed method achieves better performance on the PASCAL detection benchmark.

关键词： Feature extraction Kernel Object detection Agriculture Forestry Semantics big data

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A Prediction-Correction Based Iterative Convolution-Thresholding Method for Topology Optimization of Heat Transfer Problems

SSRN

引用

SSRN 2024年

作者： Chen, Huangxin Dong, Piaopiao Wang, Dong Wang, Xiao-Ping School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian361005 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong 518172 China

In this paper, we propose an iterative convolution-thresholding method (ICTM) based on prediction-correction for solving the topology optimization problem in steady-state heat transfer equations. The problem is formulated as a constrained minimization problem of the complementary energy, incorporating a perimeter/surface-area regularization term, while satisfying a steady-state heat transfer equation. The decision variables of the optimization problem represent the domains of different materials and are represented by indicator functions. The perimeter/surface-area term of the domain is approximated using Gaussian kernel convolution with indicator functions. In each iteration, the indicator function is updated using a prediction-correction approach. The prediction step is based on the variation of the objective functional by imposing the constraints, while the correction step ensures the monotonically decreasing behavior of the objective functional. Numerical results demonstrate the efficiency and robustness of our proposed method, particularly when compared to classical approaches based on the ICTM. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Utility maximization for multihop wireless networks employing BATS codes

arXiv

引用

arXiv 2021年

作者： Dong, Yanyan Jin, Sheng Chen, Yanzuo Yang, Shenghao Yin, Hoover H.F. The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Carnegie Mellon University The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China

BATS (BATched Sparse) codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding. Due to both the probably nonconcave objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems cannot be applied directly to solve our NUM problem. We introduce a two-step algorithm to solve our NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm. © 2021, CC BY-NC-ND.

关键词： Packet loss

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Designing of Highly-Efficient Oxygen Evolution Reaction Electrocatalyst-Transition Metal Hydroxyl Phosphates: Theory and Experiment

SSRN

引用

SSRN 2022年

作者： Xia, Junyi Li, Can Gong, Yinyan Niu, Lengyuan Chen, Miaogen Xu, Shiqing Institute of Optoelectronic Materials and Devices College of Optical and Electronic Technology China Jiliang University Hangzhou310018 China School of Electronic Science & Engineering Southeast University Nanjing210096 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou310018 China

The development of a physical factor to correlate the experimental methods and catalytic activity of catalysts is quite important for the design of highly-efficient catalysts. In this work, with the aid of our pre-designed correlation factor of d electron density, a new oxygen evolution reaction electrocatalyst of FeCoPO4 was designed on account of the higher electronegativity of Co atom than Fe atom. The foreign Co atom would enhance the combining capacities of the neighbouring Fe-O bonds, increase the d electron density and reduce the chemical activity of host Fe ions, and thus improve the catalytic activity of Fe based active centers. Based on the theoretical guidance, the prepared FeCoPO4 specimen exhibited low ηexp of 241 mV, fast charge transfer rate and stable electrocatalytic activity. This strategy would be adopted to design highly-efficient catalyst by altering the d electron density of transition metal ions with suitable preparation methods. © 2022, The Authors. All rights reserved.

关键词： Electrocatalysts

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Attentional Neural Fields for Crowd Counting

Attentional Neural Fields for Crowd Counting

引用

International Conference on Computer Vision (ICCV)

作者： Anran Zhang Lei Yue Jiayi Shen Fan Zhu Xiantong Zhen Xianbin Cao Ling Shao School of Electronic and Information Engineering Beihang University Beijing China Inception Institute of Artificial Intelligence Abu Dhabi UAE Ministry of Industry and Information Technology of China Key Laboratory of Advanced Technology of Near Space Information System (Beihang University) Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Crowd counting has recently generated huge popularity in computer vision, and is extremely challenging due to the huge scale variations of objects. In this paper, we propose the Attentional Neural Field (ANF) for crowd counting via density estimation. Within the encoder-decoder network, we introduce conditional random fields (CRFs) to aggregate multi-scale features, which can build more informative representations. To better model pair-wise potentials in CRFs, we incorperate non-local attention mechanism implemented as inter- and intra-layer attentions to expand the receptive field to the entire image respectively within the same layer and across different layers, which captures long-range dependencies to conquer huge scale variations. The CRFs coupled with the attention mechanism are seamlessly integrated into the encoder-decoder network, establishing an ANF that can be optimized end-to-end by back propagation. We conduct extensive experiments on four public datasets, including ShanghaiTech, WorldEXPO 10, UCF-CC-50 and UCF-QNRF. The results show that our ANF achieves high counting performance, surpassing most previous methods.

关键词： Estimation Task analysis Feature extraction Computational modeling Adaptation models Aggregates Machine learning

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BioT5: Enriching Cross-modal Integration in Biology with Chemical knowledge and Natural Language Associations

arXiv

引用

arXiv 2023年

作者： Pei, Qizhi Zhang, Wei Zhu, Jinhua Wu, Kehan Gao, Kaiyuan Wu, Lijun Xia, Yingce Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China China University of Science and Technology of China China Huazhong University of Science and Technology China Microsoft Research United States Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose BioT5, a comprehensive pretraining framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. BioT5 utilizes SELFIES for 100% robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, BioT5 distinguishes between structured and unstructured knowledge, leading to more effective utilization of information. After fine-tuning, BioT5 shows superior performance across a wide range of tasks, demonstrating its strong capability of capturing underlying relations and properties of bio-entities. Our code is available at https://***/QizhiPei/BioT5. Copyright © 2023, The Authors. All rights reserved.

关键词： Integration

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Median query research in wireless sensor networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2010年第2A期38卷 133-137页

作者： Wu, Zhong-Bo Zhang, Hui Chen, Hong Information School Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China School of Mathematics and Computer Science Xiangfan University Xiangfan Hubei 441053 China

Poor quality and harsh condition can result in faulty and outlier data in sampling data of sensor nodes. So we need median query to reflect average level of monitoring region. First, we put forward HMA algorithm. Second, we extend HMA algorithm and put forward HFMA algorithm. In HFMA, We only need collect data inside filter and aggregate influence coefficient during sampling period. Base station can compute median result according to the sample data inside filter and influence coefficient aggregation value. Experimental results have shown that HFMA outperforms Naive algorithm and HMA algorithm and can prolong the lifetime of sensor network.

关键词： Sensor nodes

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