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Abstraction engineering

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arXiv 2024年

作者： Bencomo, Nelly Cabot, Jordi Chechik, Marsha Cheng, Betty H.C. Combemale, Benoit Wąsowski, Andrzej Zschaler, Steffen Durham University Computer Science Durham Durham United Kingdom Luxembourg Institute of Science and Technology University of Luxembourg Esch-sur-Alzette Luxembourg University of Toronto Department of Computer Science TorontoON Canada Michigan State University Department of Computer Science and Engineering 428 S. Shaw Lane 4115 Engineering Building East LansingMI48824 United States University of Rennes IRISA and Inria Campus de Beaulieu Rennes35042 France IT University of Copenhagen Department of Computer Science Rued Langgaards Vej 7 Copenhagen2300 Denmark King’s College London Department of Informatics Bush House 30 Aldwych LondonWC2B 4BG United Kingdom

Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development—from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction engineering—a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research. © 2024, CC BY.

关键词： Application programs

Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction

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Chemosphere 2023年第1期334卷 138980页

作者： Hai, Tao Alshahri, Abdullah H. Mohammed, Amin Salih Sharma, Aman Almujibah, Hamad R. Mohammed Metwally, Ahmed Sayed Ullah, Mirzat School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Electronics and Information Engineering Ankang University Ankang China Universiti Teknologi MARA Shah Alam40450 Malaysia Department of Civil Engineering College of Engineering Taif University P.O. Box 11099 Taif City21974 Saudi Arabia Department of Software and Informatics Engineering College of Engineering Salahaddin University-Erbil Kurdistan Region Iraq Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Institute of Engineering and Technology GLA University UP Mathura281406 India Department of Mathematics College of Science King Saud University Riyadh11451 Saudi Arabia Graduate School of Economics and Management Ural Federal University Yekaterinburg620002 Russia

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h. © 2023

关键词： Gasification

Visualizing Quantum Circuit Probability estimating computational action for quantum program synthesis

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arXiv 2023年

作者： Bach, Bao Gia Kundu, Akash Acharya, Tamal Sarkar, Aritra Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Viet Nam Viet Nam Joint Doctoral School Silesian University of Technology Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Independent Researcher Bengaluru India Quantum Machine Learning Research group Quantum Computing Division QuTech Netherlands Quantum Intelligence Research team Department of Quantum & Computer Engineering Delft University of Technology Netherlands

This research applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. A tutorial-style introduction to states and various notions of the complexity of states are presented. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select some characteristic sets. The reachability and expressibility in a space-time-bounded setting for these gate sets are enumerated and visualized. These results are studied in terms of computational resources, universality and quantum behavior. The article suggests how applications like geometric quantum machine learning, novel quantum algorithm synthesis and quantum artificial general intelligence can benefit by studying circuit probabilities. © 2023, CC BY-NC-SA.

关键词： Machine learning

Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder

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arXiv 2023年

作者： Das, Manisha Gupta, Deep Radeva, Petia Bakde, Ashwini M. The Department of Electronics and Comm. Engineering Visvesvaraya National Institute of Technology Nagpur40010 India The Departament de Mathematics and Informatics Universitat de Barcelona Barcelona08007 Spain The Computer Vision Center Cerdanyola08193 Spain The Department of Radio-Diagnosis All India Institute of Medical Sciences Nagpur441108 India

Medical image fusion integrates the complementary diagnostic information of the source image modalities for improved visualization and analysis of underlying anomalies. Recently, deep learning-based models have excelled the conventional fusion methods by executing feature extraction, feature selection, and feature fusion tasks, simultaneously. However, most of the existing convolutional neural network (CNN) architectures use conventional pooling or strided convolutional strategies to downsample the feature maps. It causes the blurring or loss of important diagnostic information and edge details available in the source images and dilutes the efficacy of the feature extraction process. Therefore, this paper presents an end-to-end unsupervised fusion model for multimodal medical images based on an edge-preserving dense autoencoder network. In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps. This helps in preserving the fine edge detail information present in both the source images and enhances the visual perception of fused images. Further, the proposed model is trained on a variety of medical image pairs which helps in capturing the intensity distributions of the source images and preserves the diagnostic information effectively. Substantial experiments are conducted which demonstrate that the proposed method provides improved visual and quantitative results as compared to the other state-of-the-art fusion methods. © 2023, CC BY.

关键词： Feature extraction

Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

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Mqtt Routing Optimizing Based Intrusion Detection for Intern...

Decision Aid Sciences and Application (DASA), International Conference on

作者： Haider K. Hoomod Mohammad Alaa Al-Hamami Aqeel Kamil Kadhim Qutaiba Humadi Mohammed Jolan Rokan Niaf College of Education Mustansiriyah University Baghdad Iraq Department of Management Information Systems Applied Science University Manama Kingdom of Bahrain Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network.

关键词： Transport protocols Network intrusion detection Machine learning Routing Data transfer Data models Delays Classification algorithms Internet of Things Intelligent sensors

EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

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IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

Dynamic hidden-variable network models

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Physical Review E 2021年第5期103卷 052307-052307页

作者： Harrison Hartle Fragkiskos Papadopoulos Dmitri Krioukov Network Science Institute Northeastern University Boston 02115 Massachusetts USA Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Northeastern University Departments of Physics Mathematics and Electrical & Computer Engineering Boston 02115 Massachusetts USA

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.

关键词： Complex systems Evolving networks Network structure Block models Evolving network models Fitness models

Increasing Fairness via Combination with Learning Guarantees

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arXiv 2023年

作者： Bian, Yijun Zhang, Kun Qiu, Anqi Chen, Nanguang The Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore119276 Singapore School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Department of Biomedical Engineering National University of Singapore Singapore Research Institute National University of Singapore Suzhou China The Department of Biomedical Engineering The Johns Hopkins University BaltimoreMD United States The Department of Health Technology and Informatics The Hong Kong Polytechnic University Hung Hom Hong Kong

The concern about underlying discrimination hidden in machine learning (ML) models is increasing, as ML systems have been widely applied in more and more real-world scenarios and any discrimination hidden in them will directly affect human life. Many techniques have been developed to enhance fairness including commonly-used group fairness measures and several fairness-aware methods combining ensemble learning. However, existing fairness measures can only focus on one aspect-either group or individual fairness, and the hard compatibility among them indicates a possibility of remaining biases even if one of them is satisfied. Moreover, existing mechanisms to boost fairness usually present empirical results to show validity, yet few of them discuss whether fairness can be boosted with certain theoretical guarantees. To address these issues, we propose a fairness quality measure named discriminative risk to reflect both individual and group fairness aspects. Furthermore, we investigate the properties of the proposed measure and propose first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees. The analysis is suitable for both binary and multi-class classification. A pruning method is also proposed to utilise our proposed measure and comprehensive experiments are conducted to evaluate the effectiveness of the proposed methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning