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arXiv 2025年

作者： Kojaku, Sadamori Mahari, Robert Lera, Sandro Claudio Moro, Esteban Pentland, Alex Ahn, Yong-Yeol Department of Systems Science and Industrial Engineering Binghamton University New York United States Media Lab Massachusetts Institute of Technology Cambridge United States Harvard Law School Cambridge United States Connection Science Massachusetts Institute of Technology Cambridge United States Southern University of Science and Technology Shenzhen China Network Science Institute Department of Physics Northeastern University Boston United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes [1–3], primarily function as a way to explicitly document the use of prior work and thus have become central to the study of knowledge systems [4–6]. Analyzing citation dynamics has revealed statistical patterns that shed light on knowledge production, recognition, and formalization [5, 7–14], and has helped identify key mechanisms driving these patterns [5, 15, 16]. However, most quantitative findings are confined to scientific citations, raising the question of universality of these findings. Moreover, existing models of individual citation trajectories fail to explain phenomena such as delayed recognition, calling for a unifying framework. Here, we analyze a newly available corpus of U.S. case law, in addition to scientific and patent citation networks, to show that they share remarkably similar citation patterns, including a heavy-tailed distribution of sleeping beauties. We propose a holistic model that captures the three core mechanisms driving collective dynamics and replicates the elusive phenomenon of delayed recognition. We demonstrate that the model not only replicates observed citation patterns, but also better predicts future successes by considering the whole system. Our work offers insights into key mechanisms that govern large-scale patterns of collective human knowledge systems and may provide generalizable perspectives on discovery and innovation across domains. © 2025, CC BY-NC-SA.

关键词： Patents and inventions

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Diversity and stylization of the contemporary user-generated visual arts in the complexity-entropy plane

arXiv

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arXiv 2024年

作者： Kim, Seunghwan Lee, Byunghwee Lee, Wonjae Graduate School of Culture Technology KAIST Daejeon34141 Korea Republic of Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute of Basic Science Sungkyunkwan University Suwon16419 Korea Republic of

The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Bēhance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time. © 2024, CC BY.

关键词： Deep neural networks

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A ROS-Based Autonomous Vehicle Testbed for the Internet of Vehicles

A ROS-Based Autonomous Vehicle Testbed for the Internet of V...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Nikolaos Sarantinoudis George Tsinarakis Lefteris Doitsidis Savvas Chatzichristofis George Arampatzis School of Production Engineering and Management Technical University of Crete Chania Crete Greece Department of Computer Science Intelligent Systems Laboratory (ISLab) Neapolis University Pafos Cyprus

This paper describes the design and implementation procedure of a Robot Operating System (ROS) testbed for research on the concept of Internet of Vehicles and the integration of Digital Twins in autonomous vehicles. The proposed testbed integrates communication devices, sensors, and micro-controllers to collect real-time data from the physical environment, enabling the replication of such systems in a virtual environment using Digital Twins. The framework provides a hands-on approach for researchers and students to understand the operation and control of autonomous vehicles without requiring full-scale vehicles. The interactive and immersive learning experience simulates a real-sized vehicle, allowing users to explore the effects of different control algorithms and sensor configurations. Developed using open-source software such as ROS and low-cost microcontrollers and sensors, the platform is affordable for research and development, providing an engaging and practical learning experience.

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Chinese E-Romance: Analyzing and Visualizing 7.92 Million Alibaba Valentine's Day Purchases

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Data and Information Management 2021年第4期5卷 363-371页

作者： Wang, Yongzhen Liu, Xiaozhong Ju, Yingnan Börner, Katy Lin, Jun Sun, Changlong Si, Luo WISE Lab Institute of Science of Science and S&T Management Faculty of Humanities and Social Sciences Dalian University of Technology Dalian China Computer Science Department Worcester Polytechnic Institute WorcesterMA United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Alibaba DAMO Academy Hangzhou China

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been an explosive growth in e-commerce sales during Valentine's Day over the recent years. This exploratory study investigates the extent to which each product category and each shopper group can exhibit romantic love within China's e-market throughout the 2 weeks leading up to 2019 Valentine's Day. Massive data from Alibaba, the biggest e-commerce retailer worldwide, are utilized to formulate an innovative romance index (RI) to quantitatively measure e-romantic values for products and shoppers. On this basis, millions of shoppers, along with their millions of products purchased around Valentine's Day, are analyzed as a case study to demonstrate their love consumption and romantic gift-giving. The results of the analysis are then illustrated to help understand Chinese e-romance based on the perspectives of different product categories and shopper groups. This empirical information visualization also contributes to improving the segmentation, targeting, and positioning of China's e-market for Valentine's Day. © 2021 Yongzhen Wang et al., published by Sciendo

关键词： Sales

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Thematic Event Extraction Based on Event-Related Sentence Detection

Thematic Event Extraction Based on Event-Related Sentence De...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xing Zhang Yucan Guo Xiaolong Jin Haozhe Liang Saiping Guan School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE.

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VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

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Frontiers of computer science 2023年第6期17卷 13-24页

作者： Xumeng WANG Ziliang WU Wenqi HUANG Yating WEI Zhaosong HUANG Mingliang XU Wei CHEN College of Computer Science Nankai UniversityTianjin 300381China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Digital Grid Research Institute China Southern Power GridGuangzhou 510670China Huawei Technologies Co. Ltd.Hangzhou 310052China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Engineering Research Center of Ministry of Education on Intelligent Swarm Systems Zhengzhou UniversityZhengzhou 450001China National Supercomputing Center in Zhengzhou Zhengzhou 450001China Laboratory of Art and Archaeology Image Zhejiang UniversityHangzhou 310058China

Visualization and artificial intelligence(AI)are well-applied approaches to data *** one hand,visualization can facilitate humans in data understanding through intuitive visual representation and interactive *** the other hand,AI is able to learn from data and implement bulky tasks for *** complex data analysis scenarios,like epidemic traceability and city planning,humans need to understand large-scale data and make decisions,which requires complementing the strengths of both visualization and *** studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as ***,how can AI and visualization complement each other and be integrated into data analysis processes are still *** this paper,we define three integration levels of visualization and *** highest integration level is described as the framework of VIS+AI,which allows AI to learn human intelligence from interactions and communicate with humans through visual *** also summarize future directions of VIS+AI to inspire related studies.

关键词： visualization artificial intelligence data analysis knowledge generation

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CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

arXiv

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arXiv 2023年

作者： Jiang, Xiaoheng Guo, Kaiyi Lu, Yang Yan, Feng Liu, Hao Cao, Jiale Xu, Mingliang Tao, Dacheng School of Computer Science 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 School of Electrical and Information Engineering Tianjin University Tianjin China Sydney AI Centre the School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multilevel CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface defects

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An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm 1

An Efficient Algorithm to Classify the Mitotic Cell using An...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Vidhya, R.G. Seetha, J. Ramadass, Sudhir Dilipkumar, S. Sundaram, Ajith Saritha, G. Hkbk College of Engineering Department of Ece Bangalore India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Data Analyst Sterck Systems Chennai India Vit Department of Analytics Scope Vellore India School of Business Selection Grade Kochi India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India

ISBN: (纸本)9798350397840

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer;however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful. © 2022 IEEE.

关键词： Deposits

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Password Manager with Multi-Factor Authentication

Password Manager with Multi-Factor Authentication

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Networking and Communications (ICNWC), International Conference on

作者： R. Dhanalakshmi N. Vijayaraghavan S. Narasimhan Saleem Basha School of Computer science and Engineering Vellore Institute of Technology Chennai India Department of Mathematics KCG College of Technology Chennai India Computing and informatics department Mazoon University college muscat oman

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user’s data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication.

关键词： Privacy Data privacy Multi-factor authentication computer bugs Passwords Organizations Network security

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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