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Beyond federated learning: On confidentiality-critical machine learning applications in industry

Procedia computer science 2021年 180卷 734-743页

作者： Werner Zellinger Volkmar Wieser Mohit Kumar David Brunner Natalia Shepeleva Rafa Gálvez Josef Langer Lukas Fischer Bernhard Moser Software Competence Center Hagenberg GmbH (SCCH) Softwarepark 21 4232 Hagenberg Austria Faculty of Computer Science and Electrical Engineering University of Rostock Universitätsplatz 1 18051 Rostock Germany Computer Security and Industrial Cryptography KU Leuven Oude Markt 13 3000 Leuven Belgien ventopay GmbH Softwarepark 37 4232 Hagenberg Austria

Federated machine learning frameworks, which take into account confidentiality of distributed data sources are of increasing interest in smart manufacturing. However, the scope of applicability of most such frameworks is restricted in industrial settings due to limitations in the assumptions on the data sources involved. In this work, first, we shed light on the nature of this arising gap between current federated learning and requirements in industrial settings. Our discussion aims at clarifying related notions in emerging sub-disciplines of machine learning, which are partially overlapping. Second, we envision a new confidentiality-preserving approach for smart manufacturing applications based on the more general setting of transfer learning, and envision its implementation in a module-based platform.

关键词： machine learning federated learning collaborative learning transfer learning smart manufacturing

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Structure Function Based Transform Features for Behavior-Oriented Social Media Image Classification 5th

Structure Function Based Transform Features for Behavior-Ori...

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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Krishnani, Divya Shivakumara, Palaiahnakote Lu, Tong Pal, Umapada Ramachandra, Raghavendra International Institute of Information Technology Naya Raipur Naya RaipurChhattisgarh India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783030414030

Social media has become an essential part of people to reflect their day to day activities including emotions, feelings, threatening and so on. This paper presents a new method for the automatic classification of behavior-oriented images like Bullying, Threatening, Neuroticism-Depression, Neuroticism-Sarcastic, Psychopath and Extraversion of a person from social media images. The proposed method first finds facial key points for extracting features based on a face detection algorithm. Then the proposed method labels face regions as foreground and other than face region as background to define context between foreground and background information. To extract context, the proposed method explores Structural Function based Transform (SFBT) features, which study variations on pixel values. To increase discriminating power of the context features, the proposed method performs clustering to integrate the strength of the features. The extracted features are then fed to Support Vector Machines (SVM) for classification. Experimental results on a dataset of six classes show that the proposed method outperforms the existing methods in terms of confusion matrix and classification rate. © Springer Nature Switzerland AG 2020.

关键词： Classification (of information)

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Detection and Prevention of Jellyfish Attacks Using kNN Algorithm and Trusted Routing Scheme in MANET

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International Journal of Network Security 2021年第1期23卷 77-87页

作者： Zardari, Zulfiqar Ali He, Jingsha Pathan, Muhammad Salman Qureshi, Sirajuddin Hussain, Muhammad Iftikhar Razaque, Fahad He, Peng Zhu, Nafei Faculty of Information Technology Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing100124 China College of Computer and Information Science China Three Gorges University YichangHubei443002 China

Mobile ad hoc networks (MANETs) are surrounded by various vulnerabilities and attacks due to open medium, dynamic topology, limited energy, and absence of central control. In MANET, each attack has different behavior and aftermaths. DoS attack is one of the serious attacks in MANET, which disturbs the normal routing process. Jellyfish (JF) attack is a type of DoS attack in MANET, whose dynamic behavior makes it quite difficult to detect such attacks. In this paper, we propose a technique to detect the jellyfish attack in MANET. The proposed technique combines the authentication and trustworthiness of nodes and the kNN algorithm for the identification of jellyfish attacks in which each and every node calculates the primary and secondary trust values to detect the attacking node by the recommendation of neighboring nodes and trust metrics. The kNN algorithm separates the jellyfish nodes from other legitimate nodes based on the differences in their behavior. The proposed technique would then pick reliable nodes by the hierarchical trust assessment property of nodes to perform packet routing. The experiment shows that the proposed technique could decrease delay and increase the throughput of the network by avoiding jellyfish nodes. © 2021

关键词： Mobile ad hoc networks

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Manifold regularized dynamic network pruning

arXiv

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arXiv 2021年

作者： Tang, Yehui Wang, Yunhe Xu, Yixing Deng, Yiping Xu, Chao Tao, Dacheng Xu, Chang Dept. of Machine Intelligence Peking University China Noah’s Ark Lab Huawei Technologies China Central Software Institution Huawei Technologies China School of Computer Science Faculty of Engineering University of Sydney Australia

Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achieves higher acceleration. Most of the existing methods discover effective sub-networks for each instance independently and do not utilize the relationship between different inputs. To maximally excavate redundancy in the given network architecture, this paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks (dubbed as ManiDP). We first investigate the recognition complexity and feature similarity between images in the training set. Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost compared to the state-of-the-art methods. For example, our method can reduce 55.3% FLOPs of ResNet-34 with only 0.57% top-1 accuracy degradation on ImageNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Network architecture

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Mining architecture tactics and quality attributes knowledge in stack overflow

arXiv

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arXiv 2021年

作者： Bi, Tingting Liang, Peng Tang, Antony Xia, Xin School of Computer Science Wuhan University Wuhan430072 China Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Faculty of Information Technology Monash University MelbourneVIC3166 Australia Software and Services Research Group Vrije Universiteit Amsterdam Amsterdam1101 Netherlands

Context: Architecture Tactics (ATs) are architectural building blocks that provide general architectural solutions for addressing Quality Attributes (QAs) issues. Mining and analyzing QA-AT knowledge can help the software architecture community better understand architecture design. However, manually capturing and mining this knowledge is labor-intensive and difficult. Objective: Using Stack Overflow (SO) as our source, our main goals are to effectively mine such knowledge;and to have some sense of how developers use ATs with respect to QA concerns from related discussions. Methods: We applied a semi-automatic dictionary-based mining approach to extract the QA-AT posts in SO. With the mined QA-AT posts, we identified the relationships between ATs and QAs. Results: Our approach allow us to mine QA-AT knowledge effectively with an F-measure of 0.865 and Performance of 82.2%. Using this mining approach, we are able to discover architectural synonyms of QAs and ATs used by designers, from which we discover how developers apply ATs to address quality requirements. Conclusions: We make two contributions in this work: First, we demonstrated a semi-automatic approach to mine ATs and QAs from SO posts;Second, we identified little-known design relationships between QAs and ATs and grouped architectural design considerations to aid architects make architecture tactics design decisions. © 2021, CC BY.

关键词： Data mining

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A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm

arXiv

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arXiv 2022年

作者： Liu, Qiqi Yan, Yuping Ligeti, Péter Jin, Yaochu Trustworthy and General AI Lab School of Engineering Westlake University China Faculty of Technology Bielefeld University Bielefeld33619 Germany Faculty of Informatics Department of Computeralgebra Eötvös Loránd University Hungary Smart Data Group E-Group ICT Software Zrt Hungary Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom

Data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization problems. However, most data-driven evolutionary algorithms are centralized, causing privacy and security concerns. Existing federated Bayesian optimization algorithms and data-driven evolutionary algorithms mainly protect the raw data on each client. To address this issue, this paper proposes a secure federated data-driven evolutionary multiobjective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server. We select the query points on a randomly selected client at each round of surrogate update by calculating the acquisition function values of the unobserved points on this client, thereby reducing the risk of leaking the information about the solution to be sampled. In addition, since the predicted objective values of each client may contain sensitive information, we mask the objective values with Diffie-Hellman-based noise, and then send only the masked objective values of other clients to the selected client via the server. Since the calculation of the acquisition function also requires both the predicted objective value and the uncertainty of the prediction, the predicted mean objective and uncertainty are normalized to reduce the influence of noise. Experimental results on a set of widely used multi-objective optimization benchmarks show that the proposed algorithm can protect privacy and enhance security with only negligible sacrifice in the performance of federated data-driven evolutionary optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Neural Representations of Natural Language 1st ed. 2019

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丛书名： Studies in Computational Intelligence

2019年

作者： Lyndon White Roberto Togneri Wei Liu Mohammed Bennamoun

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Semi-automated Construction of Air Pollution Domain Ontology 1

引用

9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo Zhang, Jiahui Li, Jianqiang Qu, Guangzhi Li, Yong Lang, Jianlei School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China

ISBN: (数字)9789811532504

ISBN: (纸本)9789811532498

Air pollution has become a substantial environmental issue in China, which has seriously affected human health. To clarify the relationship among air pollutants, pollution sources, influencing factors, evaluation indicators and harms, it is essential to build a domain ontology for air pollution. Domain ontologies have been gradually accepted as a method to indicate the relationship between terms. However, there is not a complete ontology in air pollution domain, and building domain ontologies manually is time-consuming and inconvenient. In this paper, a semi-automatic approach is presented to build an ontology in air pollution domain. This paper proposes a method of entity relationship joint extraction based on attention mechanism, which is combined with core concept mining method to extract knowledge, and then concepts, relationships, and relevant instances are organized in hierarchy. Finally, the ontology model is constructed semi-automatically and semantic inference is also carried out. The research shows that this knowledge extraction method can avoid the error accumulation caused by entity recognition and relationship extraction and deepen the inner connection between them, and a large amount of effective knowledge in the field of air pollution can be extracted. In addition, the ontology constructed based on this method can also visually analyze the relationship between various classes and concepts in the field of air pollution, deduce the pollutant propagation path, and provide practical experience for the semi-automatic ontology construction in other fields and data support for further research. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ontology

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Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters

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Heliyon 2024年第21期10卷 e39901页

作者： Gatla, Ranjith Kumar Kumar, Devineni Gireesh Shashavali, Palthur Dsnm, Rao Kotb, Hossam Alkuhayli, Abdulaziz Ghadi, Yazeed Yasin Mbasso, Wulfran Fendzi Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Telangana Dundigal 500043 India Department of Electrical & Electronics Engineering B V Raju Institute of Technology Telangana Narsapur 502313 India Department of EEE S K University College of Engineering & Technology Andhra Pradesh Ananthapuramu 515003 India Department of EEE Gokaraju Rangaraju Institute of Engineering & Technology Telangana Bachupally 500090 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria 21544 Egypt Electrical Engineering Department College of Engineering King Saud University Riyadh 11421 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 15322 United Arab Emirates Laboratory of Technology and Applied Sciences University Institute of Technology University of Douala PO Box: 8698 Douala Cameroon

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults. This paper provides a comprehensive overview of the fundamental principles of multi-level inverters and the various sorts of faults that can occur in multi-level inverters. This study provides a comprehensive analysis of five-level cascaded H-bridge multilevel inverters (MLIs) under both normal and defective conditions. The paper outlines a fault-detection method that utilizes total harmonic distortion and a normalized output voltage factor. In addition, the paper discusses a fault-isolation strategy that relies on reducing amplitude modulation. This method leads to the development of a fault-tolerant inverter. The utilization of level-shifted pulse-width modulation (LSPWM) technology is employed for the purpose of switching operations. LSPWM is the most appropriate technique for MLIs that require a low amount of computational resources. The fault-diagnosis approach given is suitable for MLI-based drives, grid-connected operations, and other applications. This paper presents a comprehensive examination of the 5L-CMLI (5-Level Cascaded Multi-Level Inverter) under various fault scenarios in CMLI. Subsequently, various fault diagnosis approaches will be examined, including their advantages and disadvantages. The paper discusses several defects that can occur in the Insulated Gate Bipolar Transistor (IGBT) of a Current Mode Logic Inverter (CMLI), and also presents a design for a reliable fault diagnosis system. Furthermore, this analysis examines several fault detection strategies in CMLI, categorized according to open-loop and closed-loop dynamic systems fault classifications. © 2024 The Authors

关键词： Fault detection Multi-level inverters Open-loop and closed-loop systems Total harmonic distortion

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Embedding 3D models in offline physical environments

Embedding 3D models in offline physical environments

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作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

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