检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Kong, Chenqi Luo, Anwei Wang, Shiqi Li, Haoliang Rocha, Anderson Kot, Alex C. Lab School of Electrical and Electronic Engineering Nanyang Technology University 639798 Singapore The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China The Department of Computer Science City University of Hong Kong Hong Kong The Department of Electrical Engineering City University of Hong Kong Hong Kong Institute of Computing University of Campinas Campinas13084-851 Brazil

Digital image forensics plays a crucial role in image authentication and manipulation localization. Despite the progress powered by deep neural networks, existing forgery localization methodologies exhibit limitations when deployed to unseen datasets and perturbed images (i.e., lack of generalization and robustness to real-world applications). To circumvent these problems and aid image integrity, this paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. The rationale is grounded on the observation that most image signal processors (ISP) involve the demosaicing process, which introduces pixel correlations in pristine images. Moreover, manipulating operations, including splicing, copy-move, and inpainting, directly affect such pixel regularity. We, therefore, first split the input image into several blocks and design masked self-attention mechanisms to model the global pixel dependency in input images. Simultaneously, we optimize another local pixel dependency stream to mine local manipulation clues within input forgery images. In addition, we design novel Learning-to-Weight Modules (LWM) to combine features from the two streams, thereby enhancing the final forgery localization performance. To improve the training process, we propose a novel Pixel-Inconsistency Data Augmentation (PIDA) strategy, driving the model to focus on capturing inherent pixel-level artifacts instead of mining semantic forgery traces. This work establishes a comprehensive benchmark integrating 16 representative detection models across 12 datasets. Extensive experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints and achieve state-of-the-art generalization and robustness performances in image manipulation localization. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Empowering Marketing Management and Gaming Consumer Interaction Through AI and Citizen science

Empowering Marketing Management and Gaming Consumer Interact...

引用

IEEE Games Media Entertainment (GEM)

作者： Celestine Iwendi Chatti Subba Lakshmi Bala Dhandayuthapani Veerasamy Paul Hollins Damilare Adeola Pradeep Hewage Adedapo Paul Aderemi Piyush Kumar Shukla School of Creative Technologies University of Bolton Bolton United Kingdom Department of Computer Science & Engineering Guru Nanak Institutions Technical Campus Hyderabad Telangana India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Oman Cultural Research Development School of Arts. University of Bolton Bolton United Kingdom Department of Computer Science & Engineering University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh) Bhopal Madhya Pradesh India

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

There has been a significant revolution seen by AI getting incorporated into the management and customer relations of companies. The research of the present Artificial Intelligence (AI) revolution that influences a variety of fields i.e. video games are the topic of the article. AI systems such as machine learning and data analytics can help brands understand consumer behaviour in much greater detail; hence, companies can better reach and interest potential consumers through personalized marketing plans and campaigns. This is another case of citizen science that can host a large number of artisanal anglers who can together provide data that can make the research wider-reaching. The proposed scheme improves the level of customer accuracy and tackles trends timely as well as creates slight space for real-time communication by applying neighbour-based recommendation techniques, neural networks, and sentiment analysis. Its supremacy over the conventional methods of statistical significance is highlighted through the advent of predictive analytics and dynamic pricing approaches. The advantage of deploying natural language processing (NLP) is that it helps to understand what the customers mean with how they write. Measuring the key performance indicators at the end of this approach can be called the method of adaptation and flexibility which makes digital marketing not just refer only the success but also turn to the happiness of customers.

关键词： Video games Technological innovation Sentiment analysis Neural networks Companies Pricing Market research

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for geoscience:Progress,challenges,and perspectives

引用

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation in Multi-Cell Integrated Sensing and Communication Systems: A DRL Approach

Resource Allocation in Multi-Cell Integrated Sensing and Com...

引用

IEEE International Conference on Communications (ICC)

作者： Xiaoming Wang Huiling Wu Youyun Xu Haotong Cao Neeraj Kumar Joel J. P. C. Rodrigues School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Instituto de Telecomunicações Covilha Portugal

Integrated sensing and communication (ISAC) has been seen as a promising technology to satisfy the dual requirements of communication and sensing for the emerging applications in the next-generation wireless networks. In this paper, we research one down-link multi-cell orthogonal frequency division multiple access (OFDMA) ISAC system, in which a group of collaborative ISAC base stations send signals to their corresponding communication users, and concurrently work with multiple sensing receivers to estimate locations of multiple targets. Specifically, we investigate the joint sub-channel assignment and power allocation for users and targets to maximize the sum-rate, while ensuring the minimal signal-to-interference-plus-noise ratio (SINR) constraint for each user and the maximal Cramer-Rao lower bound (CRLB) requirement for each target. We propose a deep reinforcement learning (DRL) approach to address the above sub-channel assignment and power allocation problems. In our approach, we adopt the dueling deep Q network (DDQN) and the deep deterministic policy gradient (DDPG) network to output the sub-channel assignment policy and power allocation policy separately. Simulation results aim to prove the effectiveness of our proposed algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Language Representation with Label Knowledge for Span Extraction

Enhanced Language Representation with Label Knowledge for Sp...

引用

2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021

作者： Yang, Pan Cong, Xin Sun, Zhenyun Liu, Xingwu Institute of Computing Technology Chinese Academy of Sciences China PCG Tencent Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Mathematical Sciences Dalian University of Technology China

ISBN: (纸本)9781955917094

Span extraction, aiming to extract text spans (such as words or phrases) from plain texts, is a fundamental process in Information Extraction. Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance. However, QA Formalization does not fully exploit the label knowledge and suffers from low efficiency in training/inference. To address those problems, we introduce a new paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations. Specifically, it encodes texts and label annotations independently and then integrates label knowledge into text representation with an elaborate-designed semantics fusion module. We conduct extensive experiments on three typical span extraction tasks: flat NER, nested NER, and event detection. The empirical results show that 1) our method achieves state-of-the-art performance on four benchmarks, and 2) reduces training time and inference time by 76% and 77% on average, respectively, compared with the QA Formalization paradigm. Our code and data are available at https://***/Akeepers/LEAR. © 2021 Association for Computational Linguistics

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Hybrid WDM and Optical-CDMA over Multi-Mode Fiber Transmission System based on Optical Vortex 5

Hybrid WDM and Optical-CDMA over Multi-Mode Fiber Transmissi...

引用

5th International Conference on Electronic Design, ICED 2020

作者： Ghazi, Alaan Aljunid, S.A. Zulkarnain Syed Idrus, Syed Endut, R. Rashidi, C.B.M. Ali, N. Al-Dawoodi, Aras Fakhrudeen, Ahmed M. Fareed, Alaa Sharma, Teena School of Human Development and Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Networks Department College of Computer Science and Information Technology Kirkuk University Iraq Computer Technical Engineering Alkitab University Iraq School of Computing University Utara Malaysia Malaysia Centre of Excellence Universiti Malaysia Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Department of Electronics and Communication Engineering Malviya National Institute of Technology Jaipur India

The optical vortex has recently attracted scholars to implement it in optical tweezers, microscopy, optical communications, quantum information processing, optical trapping, and laser machining. Optical vortex beam applied classically that can be transferred to the transverse amplitude of a heralded single-photon, and optical vortex possesses a helical wavefront and carries orbital angular momentum. In this study, Optical vortex is applied in optical-CDMA (optical code-division multiple-access) in conjunction with WDM (wavelength division multiplexing). This mechanism aims to increase the capacity and security in optical communication significantly. The implementation of Laguerre-Gaussian (LG) modes with optical vortex based on one dimension zero cross-correlation (ZCC) code shows that mode coupling reduces effectively. Consequently, a positive increase in channel performance and response. Accordingly, the LG modes based on the 1D-ZCC code are investigated and propagated over multi-mode fiber (MMF) based on an optical vortex, which also substantially reduces channel effects. Consequently, all these attributes combined will result in a hybrid WDM-Optical-CDMA with an optical vortex system over MMF. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Optical tweezers

来源：评论

学校读者我要写书评

暂无评论

Detecting Violations of Conditional Functional Dependencies in Distributed Database

Detecting Violations of Conditional Functional Dependencies ...

引用

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Dattatray Raghunath Kale Amar Buchade Jagannath Nalavade Suhas Gajanan Sapate A. J. Umbarkar Department of Computer Science & Engg. MIT School of Computing MIT Art Design and Technology University Pune Vishwakarma Institute of Information Technology Pune Savitribai Phule Pune University Pune Department of Computer Science & Engg. SETI Panhala Dr. Babasaheb Ambedakar Technologycal University Lonere Department of Computer Science & Engg. Walchand College of Engineering Sangali

This paper develops a technique for detecting violations of conditional functional dependencies in distributed database systems. For a given database that has a set of functional dependencies as a data quality rule, we want to identify tuples in the database that violate these rules. When the provided database is a centralized database, there have been simple SQL-based techniques available for finding violations of the conditional functional dependencies for this particular database. However, it is more challenging to detect violations in distributed databases. Numerous works already published concentrate on functional dependency error estimation in centralized settings where data are kept in a single place, with the aim of reducing the time and space requirements of the estimation methods. With the design and execution of the heuristic algorithm, a remedy for breaches of conditional functional dependencies in the distributed database is suggested. Our key goal for the distributed data is to solve the shipment issue. To assess the efficacy of our plan and algorithm, a series of experiments are created and carried out.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Are Deep Point Cloud Classifiers Suffer From Out-of-distribution Overconfidence Issue?

Are Deep Point Cloud Classifiers Suffer From Out-of-distribu...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Xu He Keke Tang Yawen Shi Yin Li Weilong Peng Peican Zhu Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China School of Data Science Fudan University Shanghai China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Artificial Intelligence Optics and Electronics (iOPEN) Northwestern Polytechnical University Xi'an China

3D point cloud perception using deep neural networks (DNNs) has been a trend for various application scenarios. However, the black-box nature of DNNs will bring many hidden risks as in the 2D image field. In this paper, we present a preliminary evaluation on the out-of-distribution (OOD) overconfidence issue of deep point cloud classifiers, which has been proven to exist in deep 2D image classifiers, i.e., OOD inputs will lead to overconfident predictions on predefined categories. We also investigate whether a simple thresholding baseline and two modern OOD detection solutions can handle the issue by detecting OOD samples. Extensive experiments with four representative deep point cloud classifiers train/evaluate on different in/out-of-distribution point clouds validate the severity and knottiness of the OOD overconfidence issue. Our investigation will provide the groundwork for future studies on handling the OOD overconfidence issue of DNN classifiers for 3D point clouds.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine learning applications and techniques in healthcare domain: A review

引用

AIP Conference Proceedings 2024年第1期2802卷

作者： R. Mary Sheeba R. Parameswari Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

Machine Learning (ML) / Deep Learning (DL) has been applied in the development of the healthcare domain in recent years due to its increased potential and performance. It provides better support to clinicians and patients in various aspects, such as automating medical billing, clinical decision support, disease diagnosis, etc. It also plays a vital role in pattern recognition, online recommendation, finance, robotics, information security, etc. It can learn and realize the pattern from the healthcare records and gives better predictions about the disease. The goal of this review paper is to provide an overview of: (i) Machine Learning concepts; (ii) Machine Learning applications, techniques and its purpose in the healthcare domain; (iii) challenges and issues in maintaining health records; and (iv) finally, it helps to minimize the research gap with best decision support system using Machine Learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mixture of Online and Offline Experts for Non-Stationary Time Series

arXiv

引用

arXiv 2022年

作者： Zhao, Zhilin Cao, Longbing Wan, Yuanyu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computing Macquarie University Sydney Australia Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Software Technology Zhejiang University Ningbo China

We consider a general and realistic scenario involving nonstationary time series, consisting of several offline intervals with different distributions within a fixed offline time horizon, and an online interval that continuously receives new samples. For non-stationary time series, the data distribution in the current online interval may have appeared in previous offline intervals. We theoretically explore the feasibility of applying knowledge from offline intervals to the current online interval. To this end, we propose the Mixture of Online and Offline Experts (MOOE). MOOE learns static offline experts from offline intervals and maintains a dynamic online expert for the current online interval. It then adaptively combines the offline and online experts using a meta expert to make predictions for the samples received in the online interval. Specifically, we focus on theoretical analysis, deriving parameter convergence, regret bounds, and generalization error bounds to prove the effectiveness of the algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：