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arXiv 2024年

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin300072 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system’s adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community’s interest in this emerging and exciting interdisciplinary field. Cop

关键词： Federated learning

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

arXiv

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arXiv 2023年

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen China The Department of Computer Science Hong Kong Baptist University Hong Kong The Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This paper therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. Source code is available at https://***/Keke921/GCLLoss. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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An Efficient Quantum Private Comparison Protocol Based on Cluster State and Bell State 8th

An Efficient Quantum Private Comparison Protocol Based on Cl...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Li, Chaoyang Qing, Hua Xu, Gang Chen, Xiubo Xin, Xiangjun Dong, Mianxiong Ota, Kaoru College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450002 China School of Computing Science and Technology North China University of Technology Beijing100144 China Information Security Centre State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China Department of Sciences and Informatics Muroran Institution of Technology Muroran050-8585 Japan

ISBN: (纸本)9783031067631

Quantum private comparison (QPC) is the quantum method that can compare the equality of the distrustful participants’ private information without leaking them. The 4-particle cluster state has a strong violation of local reality and shows to be robust against decoherence than any other 4-particle state. In this paper, a new efficient QPC protocol based on the entanglement swapping between the 4-particle cluster state and Bell state has been proposed. With the pre-shared secret key sequence between the two participants, the semi-honest third party (TP) can only execute the protocol’s processes without obtaining information about the participants’ secrets and the final comparison results. Then, we show that the proposed QPC protocol is secure against outside or participant attacks and more efficient than similar literature. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quantum cryptography

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients

Empowering Donors: How Blockchain Technology Can Help Ensure...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Sahar Ajmal Muteeb Bin Muzammil Muhammad Shoaib Muhammad Hassaan Mehmood Department of Software Engineering National University of Computer and Emmerging Sciences Chiniot-Faisalabad Pakistan College of Computing Riphah International University Faisalabad Pakistan Department of Computer Science University of Engineering and Technology Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman Jordan

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential.

关键词： Decentralized autonomous organization Smart contracts Transforms Blockchains Consensus protocol Internet of Things Security

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Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information

arXiv

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arXiv 2023年

作者： Zhao, Kun Yang, Bohao Lin, Chenghua Rong, Wenge Villavicencio, Aline Cui, Xiaohui Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Department of Computer Science The University of Sheffield United Kingdom State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China

The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Simple KMP Pattern-Matching on Indeterminate Strings

Simple KMP Pattern-Matching on Indeterminate Strings

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2020 Prague Stringology Conference, PSC 2020

作者： Mhaskar, Neerja Smyth, W.F. Algorithms Research Group Department of Computing & Software McMaster University Canada School of Engineering & Information Technology Murdoch University WA Australia

ISBN: (纸本)9788001067499

In this paper we describe a simple, fast, space-efficient approach to finding all matches of an indeterminate pattern p = p[1..m] in an indeterminate string x = x[1..n], where both p and x are defined on a "small" ordered alphabet Σ — say, σ = |Σ| ≤ 9. A preprocessing phase replaces Σ by an integer alphabet ΣI of size σI = σ that (reversibly, in time linear in string length) maps both x and p into equivalent regular strings y and q, respectively, on ΣI, whose maximum (indeterminate) letter can be expressed in a 32-bit word (for σ ≤ 4, thus for DNA sequences, an 8-bit representation suffices). We then describe an efficient version KMP Indet of the venerable Knuth-Morris-Pratt algorithm to find all occurrences of q in y (that is, of p in x), but, whenever necessary, using the prefix array, rather than the border array, to control shifts of the transformed pattern q along the transformed string y. Although requiring O(m2n) time in the theoretical worst case, in cases of practical interest KMP Indet executes in O(n) time. A noteworthy feature is the very small additional space requirement: Θ(m) words in all cases. We conjecture that a similar approach may yield practical and efficient indeterminate equivalents to other well-known pattern-matching algorithms, especially Boyer-Moore and its variants. © PSC *** rights reserved.

关键词： Pattern matching

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RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures

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The Journal of Supercomputing 2022年第6期78卷 8890-8924页

作者： Rehman, Eid Haseeb-ud-Din, Muhammad Malik, Arif Jamal Khan, Tehmina Karmat Abbasi, Aaqif Afzaal Kadry, Seifedine Khan, Muhammad Attique Rho, Seungmin Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science HITEC University Taxila Taxila Pakistan Chung-Ang University - Seoul Campus: Chung-Ang University Seoul Korea

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, and addressing this requires designing dynamic methods and a self-adaptable scheme to discover security attacks from malicious use of IoT equipment. The best detection mechanism against attacks from compromised IoT devices includes machine learning techniques. This study emphasizes the latest literature on attack types and uses a scheme based on machine learning for network support in IoT and intrusion detection. Therefore, the current work includes a thorough analysis of multiple intelligence methods and their deployed architectures of network intrusion detection, focusing on IoT attacks and machine learning-based intrusion detection schemes. Moreover, it explores methods based on machine learning appropriate for identifying IoT devices associated with cyber attacks.

关键词：

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Feature Fusion from Head to Tail for Long-Tailed Visual Recognition

arXiv

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arXiv 2023年

作者： Li, Mengke Hu, Zhikai Lu, Yang Lan, Weichao Cheung, Yiu-Ming Huang, Hui Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China

The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head classes but largely disregard tail classes. The biased decision boundary caused by inadequate semantic information in tail classes is one of the key factors contributing to their low recognition accuracy. To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T). We replace a portion of feature maps from tail classes with those belonging to head classes. These fused features substantially enhance the diversity of tail classes. Both theoretical analysis and practical experimentation demonstrate that H2T can contribute to a more optimized solution for the decision boundary. We seamlessly integrate H2T in the classifier adjustment stage, making it a plug-and-play module. Its simplicity and ease of implementation allow for smooth integration with existing long-tailed recognition methods, facilitating a further performance boost. Extensive experiments on various long-tailed benchmarks demonstrate the effectiveness of the proposed H2T. The source code is available at https://***/Keke921/H2T. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Maori Views

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JMIR Formative Research 2024年 8卷 e59811页

作者： Jayamini, Widana Kankanamge Darsha Mirza, Farhaan Bidois-Putt, Marie-Claire Naeem, M. Asif Chan, Amy Hai Yan Department of Computer Science School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Software Engineering Faculty of Computing and Technology University of Kelaniya Kelaniya Sri Lanka School of Pharmacy Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Department of Data Science & Artificial Intelligence National University of Computer and Emerging Sciences (NUCES) Islamabad Pakistan

Background: Asthma is a significant global health issue, impacting over 500,000 individuals in New Zealand and disproportionately affecting Maori communities in New Zealand, who experience worse asthma symptoms and attacks. Digital technologies, including artificial intelligence (AI) and machine learning (ML) models, are increasingly popular for asthma risk prediction. However, these AI models may underrepresent minority ethnic groups and introduce bias, potentially exacerbating disparities. Objective: This study aimed to explore the views and perceptions that Maori have toward using AI and ML technologies for asthma self-management, identify key considerations for developing asthma attack risk prediction models, and ensure Maori are represented in ML models without worsening existing health inequities. Methods: Semistructured interviews were conducted with 20 Maori participants with asthma, 3 male and 17 female, aged 18-76 years. All the interviews were conducted one-on-one, except for 1 interview, which was conducted with 2 participants. Altogether, 10 web-based interviews were conducted, while the rest were kanohi ki te kanohi (face-to-face). A thematic analysis was conducted to identify the themes. Further, sentiment analysis was carried out to identify the sentiments using a pretrained Bidirectional Encoder Representations from Transformers model. Results: We identified four key themes: (1) concerns about AI use, (2) interest in using technology to support asthma, (3) desired characteristics of AI-based systems, and (4) experience with asthma management and opportunities for technology to improve care. AI was relatively unfamiliar to many participants, and some of them expressed concerns about whether AI technology could be trusted, kanohi ki te kanohi interaction, and inadequate knowledge of AI and technology. These concerns are exacerbated by the Maori experience of colonization. Most of the participants were interested in using technology to support their as

关键词： artificial intelligence asthma risk prediction health system development machine learning maori perceptions mobile phone

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Historical places monuments identification system

Historical places monuments identification system

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2020 IEEE Region 10 Conference, TENCON 2020

作者： Godewithana, Navod Jayasena, Khema Nagarawaththa, Chamodi Croos, Praveenth Harshanath, Buddika Alosius, Jesuthasan Faculty of Computing Sri Lanka Institute of Information Technology Department of Software Engineering Colombo Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9781728184555

Sri Lanka, which is known as 'the pearl of the Indian ocean' provides great survival and civilization history dating back to the 3rd century. Most of the archaeological sites are attracted by not only Sri Lankans but also by tourists. When searching for the information about the archaeological sites, there are lack of trusted information sources and smart online platforms. Even though some information is available, no convenient and efficient ways to retrieve them. When trusted information is provided in a user-friendly manner, the value will be added to the Sri Lankan economy. Since the world is driving towards the "E-Era", everything is involved with Information Technology. The proposed system contributes to solve the above problems with Artificial Intelligence Machine Learning concepts. The system is assisted using four major components namely, image identification, community platform, conversational bot, and image visualization. The image Identification component identifies the archaeological sites using image processing techniques. The community platform gathers trusted information from archaeologists and deep learning techniques are used to deliver that content to the users. The artificial intelligence conversational bot is established to communicate and retrieve available information in a convenient manner. The image visualization component is used to provide reality visualization on archaeological sites using the augmented reality techniques. The techniques and the algorithms are evaluated to deliver better performance with brilliant user experience. © 2020 IEEE.

关键词： Augmented reality

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