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IEEE International Conference on Systems, Man and Cybernetics

作者： P. Winzer J. Sitte Department of Quality and Product Safety University of Wuppertal Wuppertal Germany School of Software Engineering and Data Communications Queensland University of Technology Brisbane Australia

The world appears to us as complex and difficult to comprehend. Millennia of accumulated human experience were necessary to assemble the picture of our world that we have today. The purpose of general, systems theory, is to formulate concepts and methods for analysing complex situations, processes and structures irrespective of their specific nature. At times the concepts of general systems theory appear too general to be useful. In this context it is important to remember that the purpose of these concepts is to provide us with a generic skeleton that we can adapt to the specific situation under analysis. It is like a master plan that lays out the general architecture for a construction. It helps in finding the most appropriate overall layout from which we can proceed with confidence to work out the details. We will show in this paper, how we can solve problems in general.

关键词： Systems engineering and theory Assembly Knowledge engineering Electronic components Product safety Humans Skeleton Problem-solving software engineering data communication

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Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning

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Artificial Intelligence in Geosciences 2022年第1期3卷 192-202页

作者： Yihuai Lou Lukun Wu Lin Liu Kai Yu Naihao Liu Zhiguo Wang Wei Wang Center for Hypergravity Experimental and Interdisciplinary Research Zhejiang UniversityHangzhouZhejiang 310058China MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhouZhejiang 310058China School of Software Engineering Xi’an Jiaotong UniversityXi’anShaanxi 710049China Petrochina CBM Company Limited Beijing 100028China Capiticalonline Data Service Co. LTDBeijing 100012China School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’anShaanxi 710049China School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anShaanxi 710049China PowerChina Huadong Engineering Corporation Limited HangzhouZhejiang 310014China

Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent ***,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic *** based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters ***,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale *** overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data *** firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic ***,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial ***,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and *** results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.

关键词： Irregularly sampled seismic data reconstruction Deep learning U-Net Discrete wavelet transform Convolutional block attention module

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Methodic Design of Robot Vision Systems

Methodic Design of Robot Vision Systems

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IEEE International Conference on Mechatronics and Automation

作者： Joaquin Sitte Petra Winzer School of Software Engineering and Data Communications Queensland University of Technology Brisbane Australia Department of Product Safety and Quality Bergische University of Wuppertal Wuppertal Germany

In this paper we use the design of an innovative on-board vision system for a small commercial minirobotto demonstrate the application of a demand compliant design (DeCoDe) method. Vision systems are amongst the most complex sensor systems both in nature and in engineering and thus provide an excellent arena for testing design methods. A review of current design methods for mechatronic systems shows that there are no methods that support or require a complete description of the product system. The DeCoDe method is a step towards overcoming this deflciencty. The minirobot robot design is carried from the generic vision system level down to first refinement for a minirobot vision system for visual navigation.

关键词： Design methodology Robot vision systems Machine vision Robotics and automation Orbital robotics Image sensors Robot sensing systems Mechatronics Application software Decoding

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Demand compliant design of robotic systems

Demand compliant design of robotic systems

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IEEE International Conference on Mechatronics and Automation

作者： J. Sitte P. Winzer School of Software Engineering and Data Communications Queensland University of Technology Brisbane Australia Department of Product Safety and Quality Bergische Universitat Wuppertal Wuppertal Germany

The scarcity of methods for generating innovation systematically, quickly and without mistakes is felt across the technology intensive industries. An emerging autonomous robot industry produce the most complex artefacts ever made and stands to benefit most from effective design methods. In previous work, we described a framework for demand compliant design based on the existence of a demand catalogue and a method specific design database. In this paper, we show how to apply the method to an efficient exploration of design space starting a small and ill defined set of demands. Both the demand catalogue and design data are generated iteratively during the design process. We illustrate this process with the design of a hearing module for robots.

关键词： Design methodology Service robots Space exploration Decoding Product safety Auditory system Automobiles Humans Technological innovation databases

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Mastering complexity in robot design

Mastering complexity in robot design

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

This paper describes a general procedure for the requirement compliant design of robots and how design knowledge can be captured and correlated with the stakeholder demands. The key part of the method is to make an inventory of the stakeholder demands. We have created a structured list of demands for toy and educational robots based on a survey of product currently on the market. With this list it is possible to associate robot structures, functions and processes to the various demands. Thus the designer can determine which demands relate to which structure functions and processes and vice versa what requirements are affected by a given structure, function or process.

关键词： Service robots Electrical equipment industry software engineering Educational robots Systems engineering and theory Defense industry Computer industry Product design data communication Paper technology

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Utilizing Search Intent in Topic Ontology-Based User Profile for Web Mining

Utilizing Search Intent in Topic Ontology-Based User Profile...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xujuan Zhou Sheng-tang Wu Yuefeng Li Yue Xu Raymond Y.k. Lau Peter D. Bruza School of Software Engineering and Data Communications Queensland University of Technology QLD Australia Department of Information Systems City University of Hong Kong Hong Kong China

It is well known that taking the Web user profiles into account can enhance the effectiveness of Web mining systems. However, due to the dynamic and complex nature of Web users, automatically acquiring worthwhile user profiles was found to be very challenging. Ontology-based user profile can possess more accurate user information. This research emphasizes on acquiring search intentions information. This paper presents a new approach of developing user profile for Web searching. The model considers the user's search intentions by the process of PTM (Pattern-Taxonomy Model). Initial experiments show that the user profile based on search intention is more useful than the generic PTM user profile. Developing user profile that contains user search intentions is essential for effective Web search and retrieval

关键词： Ontologies Web mining Search engines data mining Web search Information retrieval software engineering data communication Information systems Hardware

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data Mining for Lifetime Prediction of Metallic Components

Conferences in Research and Practice in Information Technolo...

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Conferences in Research and Practice in Information Technology Series 2006年 61卷 75-81页

作者： Ge, Esther Nayak, Richi Xu, Yue Li, Yuefeng School of Information Systems Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia School of Software Engineering and Data Communications Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia

The ability to accurately predict the lifetime of building components is crucial to optimizing building design, material selection and scheduling of required maintenance. This paper discusses a number of possible data mining methods that can be applied to do the lifetime prediction of metallic components and how different sources of service life information could be integrated to form the basis of the lifetime prediction model. © 2006, Australian Computer Society, Inc.

关键词： data mining

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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

Attention-Weighted Federated Deep Reinforcement Learning for...

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作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

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TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED BACKDOOR WATERMARK 12

TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED B...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ya, Mengxi Li, Yiming Dai, Tao Wang, Bin Jiang, Yong Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China The State Key Laboratory of Blockchain and Data Security Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China College of Computer Science and Software Engineering Shenzhen University China Guangzhou Intelligence Communications Technology Co. Ltd. China

Saliency-based representation visualization (SRV) (e.g., Grad-CAM) is one of the most classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity and efficiency. It can be used to interpret deep neural networks by locating saliency areas contributing the most to their predictions. However, it is difficult to automatically measure and evaluate the performance of SRV methods due to the lack of ground-truth salience areas of samples. In this paper, we revisit the backdoor-based SRV evaluation, which is currently the only feasible method to alleviate the previous problem. We first reveal its implementation limitations and unreliable nature due to the trigger generalization of existing backdoor watermarks. Given these findings, we propose a generalization-limited backdoor watermark (GLBW), based on which we design a more faithful XAI evaluation. Specifically, we formulate the training of watermarked DNNs as a min-max problem, where we find the 'worst' potential trigger (with the highest attack effectiveness and differences from the ground-truth trigger) via inner maximization and minimize its effects and the loss over benign and poisoned samples via outer minimization in each iteration. In particular, we design an adaptive optimization method to find desired potential triggers in each inner maximization. Extensive experiments on benchmark datasets are conducted, verifying the effectiveness of our generalization-limited watermark. Our codes are available at https://***/yamengxi/GLBW. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep neural networks

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Simplification and analysis of transitive trust networks

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Web Intelligence and Agent Systems 2006年第2期4卷 139-161页

作者： Jøsang, Audun Gray, Elizabeth Kinateder, Michael School of Software Engineering and Data Communications Queensland University of Technology Australia Computer Science Department Trinity College Dublin Ireland Institute of Parallel and Distributed Systems Faculty of Computer Science University of Stuttgart Germany

When transacting and interacting through open computer networks, traditional methods used in the physical world for establishing trust can no longer be used. Creating virtual network substitutes with which people, organisations and software agents can derive trust in other parties requires computerised analysis of the underlying trust networks. This article describes an approach to trust network analysis using subjective logic (TNA-SL), that consists of the three following elements. Firstly it uses a concise notation with which trust transitivity and parallel combination of trust paths can be expressed. Secondly it defines a method for simplifying complex trust networks so that they can be expressed in this concise form. Finally it allows trust measures to be expressed as beliefs, so that derived trust can be automatically and securely computed with subjective logic. We compare our approach with trust derivation algorithms that are based on normalisation such as PageRank and EigenTrust. We also provide a numerical example to illustrates how TNA-SL can be applied. © 2006 - IOS Press and the authors. All rights reserved.

关键词： Computer networks

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