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database Systems for Advanced Applications 1

丛书名： Lecture Notes in Computer science

1000年

作者： Bonghee Hong Xiaofeng Meng Lei Chen Werner Winiwarter Wei Song

ISBN: (数字)9783642402708

ISBN: (纸本)9783642402692

This book constitutes the workshop proceedings of the 18th International Conference on database Systems for Advanced Applications, DASFAA 2013, held in Wuhan, China, in April 2013. The volume contains three workshops, each focusing on specific area that contributes to the main themes of the DASFAA conference: The First International Workshop on Big data Management and Analytics (BDMA 2013), the Third International Workshop on Social Networks and Social Web (SNSM 2013) and the International Workshop on Semantic computing and Personalization (SeCoP 2013).

关键词： Information Systems Applications (incl. Internet) Artificial Intelligence Information Storage and Retrieval database Management Computer Communication Networks data Mining and Knowledge Discovery

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A comparative study of autonomous learning outlier detection methods applied to fault detection

A comparative study of autonomous learning outlier detection...

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IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015

作者： Bezerra, Clauber Gomes Costa, Bruno Sielly Jales Guedes, Luiz Affonso Angelov, Plamen Parvanov Campus EaD Federal Institute of Rio Grande Do Norte-IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande Do Norte-UFRN Brazil Lancaster University Data Science Group School of Computing and Communications LA1 4WA United Kingdom

ISBN: (纸本)9781467374286

Outlier detection is a problem that has been largely studied in the past few years due to its great applicability in real world problems (e.g. financial, social, climate, security). Fault detection in industrial processes is one of these problems. In that context, several methods have been proposed in literature to address fault detection. In this paper we propose a comparative analysis of three recently introduced outlier detection methods: RDE, RDE with Forgetting and TEDA. Such methods were applied to the data set provided in DAMADICS benchmark, a very well-known real data tool for fault detection applications. The results, however, can be extended to similar problems of the area. Therewith, in this work we compare the main features of each method as well as the results obtained with them. © 2015 IEEE.

关键词： Benchmarking

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3D human pose machines with self-supervised learning

arXiv

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arXiv 2019年

作者： Wang, Keze Lin, Liang Jiang, Chenhan Qian, Chen Wei, Pengxu School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computing Hong Kong Polytechnic University Hong Kong SenseTime Group

Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this task is quite challenging due to the diverse appearances, viewpoints, occlusions and inherently geometric ambiguities inside monocular images. Most of the existing methods focus on designing some elaborate priors /constraints to directly regress 3D human poses based on the corresponding 2D human pose-aware features or 2D pose predictions. However, due to the insufficient 3D pose data for training and the domain gap between 2D space and 3D space, these methods have limited scalabilities for all practical scenarios (e.g., outdoor scene). Attempt to address this issue, this paper proposes a simple yet effective self-supervised correction mechanism to learn all intrinsic structures of human poses from abundant images. Specifically, the proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose transformation and 3D-to-2D pose projection, to serve as a bridge between 3D and 2D human poses in a type of "free" self-supervision for accurate 3D human pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate 3D poses by transforming the pose representation from the 2D domain to the 3D domain under the sequence-dependent temporal context, while the 3D-to-2D pose projection contributes to refining the intermediate 3D poses by maintaining geometric consistency between the 2D projections of 3D poses and the estimated 2D poses. Therefore, these two dual learning tasks enable our model to adaptively learn from 3D human pose data and external large-scale 2D human pose data. We further apply our self-supervised correction mechanism to develop a 3D human pose machine, which jointly integrates the 2D spatial relationship, temporal smoothness of predictions and 3D geometric knowledge. Extensive evaluations on the Human3.6M and HumanEva-I benchmarks demonstrate the superior

关键词： Geometry

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Analysis of Panoramic Images using Deep Learning For Dental Disease Identification 3

Analysis of Panoramic Images using Deep Learning For Dental ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mallick, Mohamed Midhun Kumar, A. Govindaraju, Sakthi Kumar, A. Suresh Kandasamy, Manivel Anitha, P. Rathinam Group of Institutions Coimbaotore India Indian Biological Data Center Center for Biotechnology Database Engineer Faridabab India Galogotias University School of Computing Science and Engineering Delhi NCR India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Oasys Institute of Technology Trichy India

ISBN: (纸本)9781665462167

Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and long- lasting dental illnesses, affecting people at all stages of their lives. There are a few different sorts of dental x-rays, each of which records a different perspective on dental living systems for caries detection and has the highest symptomatic accuracy, whereas panoramic radiography has the lowest Despite this, Bitewing has a few downsides, such as patient unease. Because of the lack of knowledge among administrators, Bitewing imaging frequently results in increased patient radiation exposure as a result of the need for image retakes. On the other hand, panoramic x-ray scans, also known as Orthopantomogram (OPG), are commonly utilized to capture the whole mouth with a small amount of radiation. OPG has a low radiation content, is simple to use, takes less time, and provides exceptional patient comfort. In this way, when compared to intraoral frameworks, pediatric, impaired, and senior people would profit greatly from panoramic imaging frameworks. This research work explores panoramic photos to detect accurate dental disease using deep learning. As a result, most patients can benefit from panoramic imaging, which covers a large portion of the maxillofacial region, including all of the teeth. © 2023 IEEE.

关键词： Image enhancement

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Building a geographical ontology by using Wikipedia

Building a geographical ontology by using Wikipedia

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13th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2011

作者： Hung-Ngo, Quoc Doan, Son Winiwarter, Werner Faculty of Computer Science University of Information Technology Vietnam National University HCM City Viet Nam National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo 101-8430 Japan University of Vienna Research Group Data Analytics and Computing Universitätsstraße 5 1010 Vienna Austria

This paper introduces an approach to build a geographical ontology of countries at the global scale. Our approach is based on the free online encyclopedia Wikipedia to extract lists of places, then rebuild a hierarchy... 详细信息

ISBN: (纸本)9781450307840

关键词： Ontology

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Human perceptions on moral responsibility of AI: A case study in AI-assisted bail decision-making

arXiv

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arXiv 2021年

作者： Lima, Gabriel Grgić-Hlača, Nina Cha, Meeyoung School of Computing KAIST Data Science Group IBS Korea Republic of Max Planck Institute for Software Systems Max Planck Institute for Research on Collective Goods Germany

How to attribute responsibility for autonomous artificial intelligence (AI) systems’ actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people’s perceptions of eight different notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful difference in how people perceived these agents’ moral responsibility;human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these findings, such as the need for explainable AI in high-stakes scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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When Trust Meets the Internet of Vehicles: Opportunities, Challenges, and Future Prospects

When Trust Meets the Internet of Vehicles: Opportunities, Ch...

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International Conference on Collaborative computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Adnan Mahmood Quan Z. Sheng Sarah Ali Siddiqui Subhash Sagar Wei Emma Zhang Hajime Suzuki Wei Ni School of Computing Macquarie University NSW Australia CSIRO Data61 Cybernetics Group NSW Australia School of Computer Science The University of Adelaide SA Australia

ISBN: (纸本)9781665416269

Recent technological breakthroughs in vehicular ad hoc networks and the Internet of Things (IoT) have transformed vehicles into smart objects thus paving the way for the evolution of the promising paradigm of the Internet of Vehicles (IoV), which is an integral constituent of the modern intelligent transportation systems. Simply put, IoV attributes to the IoT-on-wheels, wherein vehicles broadcast safety-critical information among one another (and their immediate ambiences) for guaranteeing highly reliable and efficacious traffic flows. This, therefore, necessitates the need to fully secure an IoV network since a single malicious message is capable enough of jeopardizing the safety of the nearby vehicles (and their respective passengers) and vulnerable pedestrians. It is also pertinent to mention that a malicious attacker, i.e., vehicle, is not only able to send counterfeited safety-critical messages to its nearby vehicles and the traffic management authorities but could further enable a compromised vehicle to broadcast both spoofed coordinates and speed-related information. It is, therefore, of the utmost importance that malicious entities and their messages be identified and subsequently eliminated from the network before they are able to manipulate the entire network for their malicious gains. This paper, therefore, delineates on the convergence of the notion of trust with the IoV primarily in terms of its underlying rationale. It further highlights the opportunities which transpire as a result of this convergence to secure an IoV network. Finally, open research challenges, together with the recommendations for addressing the same, have been discussed.

关键词： Connected vehicles Smart cities Conferences Vehicular ad hoc networks Transportation Collaboration Safety

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Introduction to the Minitrack on Business Process Technology

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System sciences 2022年 2022-January卷 6195-6196页

作者： Leopold, Henrik van der Aa, Han Reijers, Hajo A. Department of Operations and Technology Kühne Logistics University Hamburg Germany Data and Web Science Group University of Mannheim Mannheim Germany Department of Information and Computing Sciences Utrecht University Utrecht Netherlands

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Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure

arXiv

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arXiv 2024年

作者： Deng, Zehang Sun, Ruoxi Xue, Minhui Wen, Sheng Camtepe, Seyit Nepal, Surya Xiang, Yang School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC Australia Cybersecurity and Quantum Systems Group CSIRO’s Data61. Australia

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6x to over 100x more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead. © 2024, CC BY.

关键词： Critical infrastructures

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Semantic Segmentation with Attention Dense U-Net for Lung Extraction from X-ray Images

Semantic Segmentation with Attention Dense U-Net for Lung Ex...

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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Akib Al Mahmud Auvy Rafiatul Zannah Mahbub-E-Elahi Shezhan Sharif Washik Al Mahmud Jannatun Noor Department of Computer Science and Engineering Computing for Sustainability and Social Good (C2SG) Research Group School of Data and Sciences BRAC University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Deep learning and digital image processing are crucial in medical imaging research. Lung segmentation is particularly challenging, demanding accurate differentiation of complex structures, sophisticated algorithms, and deep learning models for reliable results. In our research, We used the Shen-zhen chest X-ray dataset, comprising 566 frontal chest X-rays focused on pulmonary tuberculosis. This dataset, compiled by the Guangdong Medical College and Shenzhen No. 3 People's Hospital, was released by the United States National Library of Medicine. Images, captured using a Philips DR Digital Diagnostic system, were resized from $3000 \times 3000$ pixels to $512 \times 512$ pixels for computer-aided diagnosis research. We initially used the Attention Dense U-Net to extract lungs from chest X-rays, leveraging attention mechanisms to focus on relevant features and dense blocks to improve feature reuse. Our goal is to assess its performance for chest X-ray segmentation, comparing it with other deep learning models due to limited research in this area. So, the model is compared with four other variants of U-Net architectures to segment lung pixels from an X-ray image. After implementing the approach using a benchmark dataset and comparing it to other existing architectures, we present that Attention Dense U-Net gives the best accuracy for all given parameters, with a result of accuracy: 97.48%, Dice coefficient: 94.87%, and IoU: 93.87%.

关键词： Deep learning Tuberculosis Semantic segmentation Lung Computer architecture Feature extraction Libraries

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