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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 15-23页

作者： Qamar, Roheen Zardari, Baqir Ali Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology Nawabshah Pakistan Department of Information Technology Quaid-e-Awam University of Engineering Science and Technology Nawabshah Pakistan

Blockchain and the Internet of Things (IoT), two of the most emerging technologies, are already reconfiguring our digital future, as described by the drastic change in the current network architecture. The incorporation of IoT has brought the objects around us to life, making them "smart" and capable of communicating with one another, thereby amassing massive data by consistently acquiring the physical world for analysis and intelligent action. It has made our dream of seamless integration of the digital and physical worlds a reality, altering the essence of our perception of the physical world. However, the issue with current IoT solutions is the requirement for a centrally controlled party (similar to a cloud server). This research covers the most essential implications of IoT’s most challenging scenarios, as revealed by the current studies, when connecting and communicating via the Internet. The vast amounts of generated sensitive data pose a significant risk to privacy and security;interestingly, the original architecture design calls for a decentralized system, such as the distributed or peer-to-peer (P2P) system. This scenario highlights the relevance of the blockchain, as it can offer a secure and trustworthy method of sharing information via a distributed/P2P model for achieving transparency, security, privacy, auditability, resilience, access authentication, data immutability, and so on. In this study, we explain how blockchain and IoT technologies are combined to overcome their specific shortcomings and ultimately achieve their maximum benefits. This research also discusses the technical aspects of IoT and bitcoin. We present a comprehensive overview of both technologies, including the blockchain-based IoT (BIoT) architecture and operation, and a few examples of BIoT applications and their comparison. © 2023 Authors. All rights reserved.

关键词： Internet of things

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MA-Net: Multi-Scale Adaptive Network for Oriented Object Detection in Remote Sensing Images

MA-Net: Multi-Scale Adaptive Network for Oriented Object Det...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Pan, Jiahao Zhang, Chongyang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350337020

The remote sensing image detection task faces significant challenges, e.g. complex background, dense distribution, widely varied target scale, and diverse target directions. This paper proposes a remote sensing image Oriented object detection network (MA-Net) that integrates parallel multi-scale feature extraction and adaptive dynamic label assignment. The network is based on the FCOS object detection algorithm, which introduces a parallel multi-scale feature extraction module to enhance the model's understanding of multi-scale objects. The adaptive dynamic label assignment algorithm is introduced to enable the model to select samples that are more worthy of learning. According to the number of positive samples at different levels, hierarchical supervision reweighting is used to help the model focus on more important levels and improve detection accuracy. Experiments are conducted on two public datasets, DOTA and HRSC2016. Compared with the original FCOS algorithm, the detection accuracy of our algorithm was improved by 2.45% and 4.44%. And its performance is comparable to that of state-of-the-art detection methods. © 2023 IEEE.

关键词： Label assignment Object detection Remote sensing images

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Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

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Chinese Physics B 2023年第10期32卷 273-282页

作者：马璐陈梅辉何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health *** physiological complexity of the cardiac system is related to age,disease,*** the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time *** propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological *** obtained results suggest that these two network measures show distinct changes between different *** is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac ***,the complexity of the cardiac system is *** that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed *** of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two ***,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

关键词： electrocardiogram signals multivariate recurrence networks cross-clustering coefficient entropy multiscale analysis

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SLOG: smartphone-based semantic lifelogging framework for digital prosthetic memory development

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Personal and Ubiquitous Computing 2023年第6期27卷 2071-2102页

作者： Ali, Shaukat Khusro, Shah Khan, Akif Khan, Inayat Alam, Iftikhar Solehria, Salman Faiz Department of Computer Science University of Peshawar Peshawar25120 Pakistan Department of Computer Science Sarhad University of Information Technology Peshawar25000 Pakistan Department of Computer Science University of Engineering and Technology Mardan Mardan23200 Pakistan

The advancements in sensors, processing, storage and networking technologies have turned smartphone into de facto lifelogging device. Realizing the lifelogging potential of smartphone, researchers have postulated with smartphone-based lifelogging systems with varying capabilities of capturing and storing information about peoples’ daily life activities, behaviors, interactions, locations, environments, etc., into their lifelog archives. However, the systems are lacking with several capturing, storing, retrieving and design issues, importantly not organizing the lifelog information with the same semantics as they exist in the real world and encoded in human episodic memory. The semantic organization of lifelog information will develop a true digital prosthetic memory to solve problems in different real-world domains including memory augmentation. In this paper, we have presented the architecture and implementation of Semantic LifelOGging (SLOG) framework for smartphone-based semantic lifelogging using Semantic Web technologies. The SLOG framework incorporates smartphone sensors and processing capabilities, and Semantic Web technologies to develop a semantically enriched digital prosthetic memory on smartphone. The semantic lifelog data model is developed by extending SmartOntoSensor ontology with relevant concepts and properties to represent, store and relate lifelog and contextual information. The semantic lifelog data model will provide contextual semantics to organize, annotate and relate lifelog information like human episodic memory and provide enough contextual cues to recall lifelog information like associative recall in human memory. The proof-of-concept applications are developed to show practicality of the SLOG framework. The quantitative and empirical evaluations have shown the SLOG framework and system has improved features and is acceptable in usability and effectiveness. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part

关键词： Smartphones

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Interpretation Mode of Spaceborne SAR Based on Analysis of Cognitive Needs 11th

Interpretation Mode of Spaceborne SAR Based on Analysis of C...

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11th China Conference on Command and Control, C2 2023

作者： Lanyu, Li Shouwei, Yang Nanjing Research Institute of Electronic Technology Nanjing210049 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210004 China

ISBN: (纸本)9789819990207

The intelligent interpretation of spaceborne SAR images is an important technical field for future remote sensing applications. However, due to the imaging defects of SAR itself, it is very difficult to divide the image interpretation samples. Command and control, as an important application field of image interpretation, has many cognitive scenarios. Based on the analysis of image usage requirements, this article calculates the information value of corresponding target samples under different application purposes through text analysis of relevant SAR image scientific literature, technical reports, etc., and uses this as the selection threshold for image interpretation to improve the efficiency of image interpretation. At the same time, this paper also designed a SAR image interpretation mode based on graph neural network, replacing the attention mechanism module with knowledge calibration, and providing technical support for the in-depth application of satellite-borne SAR. © Chinese Institute of Command and Control 2024.

关键词： Synthetic aperture radar

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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YGC-SLAM:A visual SLAM based on improved YOLOv5 and geometric constraints for dynamic indoor environments

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虚拟现实与智能硬件(中英文) 2025年第1期7卷 62-82页

作者： Juncheng ZHANG Fuyang KE Qinqin TANG Wenming YU Ming ZHANG Department of Automation University of Information Science&TechnologyNanjing 210044China Department of Software University of Information Science&TechnologyNanjing 210044China Wuxi Research Institute of Nanjing University of Information Engineering Wuxi 214101China Department of Rail Transport University of WuxiWuxi 214101China

Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position *** this study,we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the *** First,the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function,whereby the prediction frame converges quickly for better *** improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic ***,multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points,causing a failure of map *** K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel ***,a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud *** Through testing on TUM dataset and a real environment,the experimental results show that our algorithm reduces the absolute trajectory error by 98.22%and the relative trajectory error by 97.98%compared with the original ORBSLAM2,which is more accurate and has better real-time performance than similar algorithms,such as DynaSLAM and *** The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects,and the system can better complete positioning and map building tasks in complex environments.

关键词： Visual SLAM Dynamic SLAM Target detection Geometric constraints

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Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

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SN computer science 2024年第5期5卷 567页

作者： Behera, Sudersan Kumar, AVS Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering & amp Technology GIET University Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approach outlined above, referred to as the Improved SAHA (ISAHA), and has the ability to produce superior outcomes. The multiplicative functional link artificial neural network (MFLANN) is an improved version of the functional link artificial neural network that promotes exploration by replacing the summing unit in the output layer with a multiplication unit. Moreover, the combination of ISAHA and MFLANN results in the development of ISAHA-MFLANN, a sophisticated hybrid forecasting model. The main assessment of the hybrid model rests on its ability to forecast complex and dynamic financial time series. It's possible to get around the problems that come with traditional learning-based MFLANN techniques by using MFLANN's advanced approximation features and ISAHA's resilient global search features together. Experimental verification using two stock market datasets and three currency exchange rates demonstrates the validity of the idea. The results show that the suggested improved SAHA hybrid learning works well at improving six standard benchmark functions. The ISAHA-MFLANN model is also statistically significant at accurately capturing the volatility that is inherent in the financial time series. In addition, it surpasses other models such as SAHA-MFLANN, Monarch Butterfly Optimization-MFLANN, Particle Swarm Optimization-MFLANN, Genetic Algorithm-MFLANN, Gradient Descent-MFLANN, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Auto Regressive Integrated Moving Average (ARIMA). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Exchange rate forecasting Improved Set Algebra-Based Heuristic Algorithm Multiplicative functional link artificial neural network Stock market forecasting

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A Note on Approximating Weighted Nash Social Welfare with Additive Valuations 51

A Note on Approximating Weighted Nash Social Welfare with Ad...

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Feng, Yuda Li, Shi Department of Computer Science and Technology Harbin Institute of Technology Heilongjiang China Department of Computer Science and Technology Nanjing University Jiangsu China

ISBN: (纸本)9783959773225

We give the first O(1)-approximation for the weighted Nash Social Welfare problem with additive valuations. The approximation ratio we obtain is e1/e + ϵ ≈ 1.445 + ϵ, which matches the best known approximation ratio for the unweighted case [3]. Both our algorithm and analysis are simple. We solve a natural configuration LP for the problem, and obtain the allocation of items to agents using a randomized version of the Shmoys-Tardos rounding algorithm developed for unrelated machine scheduling problems [30]. In the analysis, we show that the approximation ratio of the algorithm is at most the worst gap between the Nash social welfare of the optimum allocation and that of an EF1 allocation, for an unweighted Nash Social Welfare instance with identical additive valuations. This was shown to be at most e1/e ≈ 1.445 by Barman et al. [3], leading to our approximation ratio. © Yuda Feng and Shi Li.

关键词： Approximation algorithms

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Contour Refinement for Box Based Interactive Object Segmentation

Contour Refinement for Box Based Interactive Object Segmenta...

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2023 International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2023

作者： Ma, Jing Shi, Ran Shen, Xiaobo Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350311297

As the development of interactive object segmentation, the optimization of the segmented contour draws much attention. Inspired by current interactive object segmentation and contour-based methods, this paper proposes an end-to-end framework for contour refinement for box based interactive object segmentation. Compared with traditional interactive object segmentation approaches, our method not only constrains the scope of segmentation in terms of region, but also controls and optimizes the deformational results of the contour, which achieves the goal of refined interactive object segmentation. We build a complete system including a contour extraction model and a contour deformation model. The initial contour extracted from the result of segmentation by the contour extraction model is fed to the contour deformation model for vertices offset and contour deformation. Meanwhile, we perform dynamic dilation for the initial contour before contour refinement to improve the feature information provided by the contour extraction model. In addition, we put the compactness of each contour as a reference factor into the point set sampled on the contour to solve the problem of over-sharp boundary. Experiments show that the proposed method achieves advanced performance for interactive object segmentation. © 2023 IEEE.

关键词： Extraction

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