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1st IEEE International Conference on Emerging research in Computational Science, ICERCS 2023

作者： Ibrahim, Ameer Kaissar Fouad, Laith Ali Hussien, Naseer Hani Sherif, Noora Ali, Aqeel National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq

ISBN: (纸本)9798350359756

Integrated sensor fusion is a novel method in which data from various sensors is intelligently fused to enable software that enhances the performance of an application or an entire system. This technique uses a network of sensors to overcome individual sensors' limitations to provide accurate position and orientation data. It is possible to quantify the state of an entity as a data or information mixture by characterizing the merging of data. Increased confidence, dependability, and simplicity in assessing company conditions via engineering systems are only some of the benefits of this interdisciplinary area. In this research, the potential aids of data fusion in civil engineering (CE), as well as some of the more recent implementations of fusion in CE. When problems arise in an application, mathematical modeling (MM) reduces them to tractable mathematical formulations that may be used for theoretical and numerical analysis. Building planning issues were addressed using an integer linear programming mathematical model. The advent of pervasive sensor networks and communications systems has enabled MMCE to use this methodology to rapidly and accurately estimate civil systems, including traffic networks, structural systems, and construction projects. The system can quickly arrive at information-based decisions by checking its health. The implications of this are many, and the value is enormous. Information fusion is a crucial part of monitoring system health. Future applications and research requirements are highlighted. © 2023 IEEE.

关键词： Sensor networks

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Outage Performance Analysis in Cognitive RIS-NOMA System under Imperfect CSI 3

Outage Performance Analysis in Cognitive RIS-NOMA System und...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Xue, K. Li, M. Chen, W. Han, Z. Yuan, H. Taiyuan University of Science and Technology School of Electronics Information Engineering China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China University of Houston Department of Electrical and Computer Engineering United States Kyung Hee University Department of Computer Science and Engineering Korea Republic of Kingston University School of Computer Science and Mathematics KT1 2EE United Kingdom

ISBN: (纸本)9798350333732

Cognitive non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have been envisioned as two promising technologies for vehicle to everything (V2X) networks. This paper considers an RIS-aided V2X network with cognitive NOMA while considering the imperfect channel state information (CSI) due to the mobility of the vehicles. The new approximate outage probability (OP) expressions of the supposed system are derived over double Rayleigh fading channels. Numerical and Monte Carlo simulation results are presented to corroborate the analysis. The results demonstrated that imperfect CSI has a significant but different impact on the outage performance of the far and near users, which are also different under different numbers of reflecting units. © 2023 IEEE.

关键词： Monte Carlo methods

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Knowledge Graph Entity Typing with Curriculum Contrastive Learning 31

Knowledge Graph Entity Typing with Curriculum Contrastive Le...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Hao Nuo, Minghua Jiang, Shan College of Computer Science Inner Mongolia University Inner Mongolia Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Hohhot010021 China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Inner Mongolia Hohhot010021 China

ISBN: (纸本)9798891761964

The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Most recent studies only focus on the structural information from an entity's neighborhood or semantic information from textual representations of entities or relations. In this paper, inspired by curriculum learning and contrastive learning, we propose the CCLET model using the Curriculum Contrastive Learning strategy for KGET, which uses the Pre-trained Language Model (PLM) and the graph model to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) respectively. Our CCLET model consists of two main parts. In the Knowledge Fusion part, we design an Enhanced-MLP architecture to fuse the text of the entity's description, related triplet and tuples;In the Curriculum Contrastive Learning part, we define the difficulty of the training course by controlling the level of added noise, we aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. Our extensive experiments demonstrate that the CCLET model outperforms recent state-of-the-art models, verifying its effectiveness in the KGET task. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Adaptive graph residual network for hand shape estimation in single images 4

Adaptive graph residual network for hand shape estimation in...

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4th International Conference on computer Vision and Information technology, CVIT 2023

作者： Li, Rui Lu, Haopeng Cui, Chen Ma, Siwei National Engineering Research Center of Visual Technology School of Computer Science Peking University No.5 YiHeYuan Road Haidian District BeijingCN 100871 China Cooperative Medianet Innovation Center Shanghai Jiaotong University No.800 Dongchuan Road Minhang District ShanghaiCN 200030 China

ISBN: (纸本)9781510672864

A graph convolutional network (GCN) has demonstrated impressive success in hand pose and shape estimation, due to its high interpretability and powerful capability for dealing with non-Euclidean data. In traditional GCN-based hand pose and shape estimation methods, the Chebyshev spectral graph convolution is most widely-used, and it is directly introduced to a simple multilayer network. In terms of the form, this graph convolution does not resemble a standard 2D convolution on an image. In terms of the practical effect, this graph convolution equally treats a center node and its neighbors. Inspired by action recognition studies, we introduce an adaptive graph convolution to hand pose and shape estimation, which not only considers the difference between a center node and its neighbors, but also considers the edge importance. Based on the adaptive graph convolution, we design a multilayer graph residual network with a double-skip-connection architecture. Extensive ablation studies are conducted to demonstrate the improvements due to the use of the adaptive graph convolution and the advantages of the graph residual network. Our method outperforms recent baselines on the public FreiHAND hand pose and shape estimation dataset. © 2024 SPIE.

关键词： Convolution

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Improved Yolo v3 via feature map design and anchor box design

Improved Yolo v3 via feature map design and anchor box desig...

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2021 International Conference on computer Vision and Pattern Analysis, ICCPA 2021

作者： Luo, Xuze Luo, Xiaonan Liu, Tianjia Computer Science Guilin University of Electronic Technology Guangxi China Computer Science National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin541000 China

ISBN: (数字)9781510651937

ISBN: (纸本)9781510651920

We introduced a method to improve the feature map and Anchor box of Yolo V3 network on VOC data set, so as to improve the detection accuracy of target in specific data set. Meanwhile, we also improved the detection accuracy of small targets to some extent. We improved Yolo V3 network. We increased the number of feature extraction layers and deepened its network structure, so that it could better detect targets. For different data sets, we needed to adjust the size of Anchor box. We added the extraction method of some feature layers of SSD into Yolo V3 network to match relative data sets. At the same time, we use the characteristics of ResNet to solve the problem of small target distortion after multiple convolution. The modified model improved mAP by 2.93% on *** put forward three points (1) To change the original network structure, increase the number of feature layers and add convolution layers to deepen the network depth. (2) Change the scale of the prior box to match the target scale of the data set. (3) Deepen ResNet in shallow network to extract small targets, select the appropriate size of Anchor box according to specific small targets, enhance small target data and then conduct training. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Extraction

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study 6

High-Resolution Optical Imaging Incorporated Non-Destructive...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Hakmana, Nethmi Tharushi Kahatapitiya, Nipun Shantha Weerasinghe, Sasmitha Kalupahana, Deshan Silva, Bhagya Nathali Ravichandran, Naresh Kumar Seong, Daewoon Han, Sangyeob Lee, Seung Yeol June, Hee-Young Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga Faculty of Engineering Sri Lanka Institute of Information Technology Department of Mechanical Engineering Malabe Sri Lanka Faculty of Engineering University of Sri Jayewardenepura Department of Computer Engineering Nugegoda Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Korea Republic of School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Korea Republic of ICT Convergence Research Center Kyungpook National University Daegu Korea Republic of Kyungpook National University Department of Plant Medicine Daegu Korea Republic of Institute of Plant Medicine Kyungpook National University Daegu Korea Republic of Faculty of Engineering Sri Lanka Institute of Information Technology Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management. © 2024 IEEE.

关键词： Fruits

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OpticE: A Coherence Theory-Based Model for Link Prediction 29

OpticE: A Coherence Theory-Based Model for Link Prediction

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29th International Conference on Computational Linguistics, COLING 2022

作者： Gui, Xiangyu Zhao, Feng Jin, Langjunqing Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector or a point in a feature space. The distance between these points is a measure of semantic similarity. However, in a KG, while two entities may have similar semantics in some relations, they have different semantics in others. It is ambiguous to assign a fixed distance to depict the variant semantic similarity of entities. To alleviate the semantic ambiguity in KGs, we design a new embedding approach named OpticE, which is derived from the well-known physical phenomenon of optical interference. It is a lightweight and relation-adaptive model based on coherence theory, in which each entity’s semantics vary automatically regarding different relations. In addition, a unique negative sampling method is proposed to combine the multimapping properties and self-adversarial learning during the training process. The experimental results obtained on practical KG benchmarks show that the OpticE model, with elegant structures, can compete with existing link prediction methods. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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PF-Training: Parameter Freezing for Efficient On-Device Training of CNN-based Object Detectors in Low-Resource Environments

PF-Training: Parameter Freezing for Efficient On-Device Trai...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Dayoung Chun Hyuk-Jae Lee Hyun Kim Department of Electrical and Computer Engineering Inter-University Semiconductor Research Center (ISRC) Seoul National University Seoul Korea Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

There has been active research focusing on lightweight approaches for on-device CNN training. Convolutional neural network (CNN) training requires a substantial amount of computation and memory footprint, particularly when compared with inference. However, in the case of on-device training, the available resources are limited, making it particularly challenging to train CNNs on-device. This study proposes a lightweight algorithm for CNN training in low-resource environments using parameter freezing techniques. The proposed method reduces the training load by employing a batch size of one and mitigates the computational overhead by using normalization freezing and modified weight optimization techniques. Furthermore, we propose a simple algorithm based on weight distribution to select the layers for freezing, thereby enabling efficient training. The proposed method is applied to Tiny-YOLOv3, demonstrating 52.10% computation reduction, 55.79% memory footprint reduction, and 21.95% accuracy improvement compared to the fully trained fine-tuned model.

关键词： Training Accuracy Quantization (signal) Memory management Focusing Detectors Inference algorithms

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TTA-GCN: Temporal Topology Aggregation for Skeleton-Based Action Recognition 12th

TTA-GCN: Temporal Topology Aggregation for Skeleton-Based A...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Meng, Haoming Zhao, Yangfei Guo, Yijing Lv, Pei Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China

ISBN: (纸本)9783031463075

Graph Convolutional Networks (GCNs) have been widely used in skeleton-based action recognition. In GCN-based approaches, graph topology dominates feature aggregation, and therefore extraction of the complex relationships between joints is the key to generate spatial-temporal skeletal graph topology structure. We note that current methods are more inclined to construct the topology matrix from the spatial dimension and rarely combine the features from the temporal dimension. This paper proposes a Temporal Topology Aggregation Graph Convolutional Network (TTA-GCN) to learn temporal topology dynamically and efficiently aggregating topology structure in channel dimensions for skeleton-based action recognition. In addition, the multi-stream ensemble framework has a significant effect on improving action recognition accuracy, and more than single natural skeleton modality are required to fuse multi-streams. Therefore, we present a multi-modal representation according to the semantics of human skeleton to capture relationships between non-naturally connected joints. Extensive experiments show that our model results achieved state-of-the-arts performance on three large accepted datasets: NTU-RGB+D 60, NTU-RGB+D 120, and Northwestern-UCLA. Finally, we evaluated the effectiveness of our model through various comparison experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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Joint IoT Device Selection and Health-Aware Beamforming Design for MIMO-WPT

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IEEE Transactions on Mobile Computing 2024年第9期23卷 8822-8838页

作者： Wang, Chih-Hang Shi, Yishuo Yang, De-Nian Chen, Wei-Yu Chen, Wen-Tsuen Academia Sinica Institute of Information Science Taipei115201 Taiwan Wenzhou University Department of Statistics and Information Science College of Mathematics and Physics Wenzhou325035 China Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taipei115201 Taiwan University of Southern California Department of Electrical and Computer Engineering Los AngelesCA90007 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety. © 2002-2012 IEEE.

关键词： Energy harvesting

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