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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Haiderzai, Mohammad Daud Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9798400700408

User contributions is an important part in software development, the pursuit of identifying effective means of user contributions in software development is a crucial area of interest for software companies and their development teams. Determining the most opportune moments, locations, and methods for such contributions presents a significant challenge for organizations, which necessitates the exploration of generic and proven solutions. Applying patterns, established and observed solutions may provide the answers to such challenges and similar ones. This paper offers insights into the practical implementation of seven newly discovered organizational patterns in a real-life environment. The patterns are comprehensively described, accompanied by illustrative examples, and subsequently applied in actual practices. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： software design

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Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

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Intelligent Automation & Soft Computing 2023年第3期35卷 3659-3675页

作者： Mohamed Ezz Ayman Mohamed Mostafa Ayman Elshenawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Systems and Computers Engineering DEPT Faculty of EngineeringAl-Azhar UniversityCairo11651Egypt Software Engineering&Information Technology Faculty of Engineering&TechnologyEgyptian Chinese UniversityEgypt

Face authentication is an important biometric authentication method commonly used in security *** is vulnerable to different types of attacks that use authorized users’facial images and videos captured from social media to perform spooﬁng attacks and dynamic movements for penetrating secur-ity *** paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble *** proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a speciﬁc response for every authentication trial with a different sequence of *** proposed model is applied to the KDEF dataset using 10-fold *** improvements are made to the proposed ***,the VGG16 model is applied to the seven common ***,the system usability is enhanced by analyzing and selecting only the four common and easy-to-use ***,the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authen-tication ***,the Horizontal Ensemble Best N-Losses(HEBNL)is applied using challenge-response emotion to improve the authentication efﬁ-ciency and minimize the computational *** successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1%to 99.27%.

关键词： Face authentication challenge-response authentication transfer learning and horizontal ensemble technique

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Lockstep Vs Microarchitecture: A Comparison 37

Lockstep Vs Microarchitecture: A Comparison

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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava842 16 Slovakia Slovak University of Technology Institute of Informatics Information Systems and Software Engineering Bratislava842 16 Slovakia

ISBN: (纸本)9798350377569

Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple lockstep technique to the microarchitecture-level protection of the Hardisc RISC-V core. For a fair comparison, each system is based on the same core(s) and integrates protection against bit-flips in memory and transient faults in the bus. We propose a fault injection methodology, combining pre-synthesis simulation with synthesis data to analyse the vulnerability of a system to faults. The fault injection campaigns show that the Hardisc can withstand fault rates orders of magnitude higher than the dual-core lockstep system while preserving the same area and power consumption. It comes with a 5% frequency penalty. We have shown that system failures are more frequent once the fault rate reaches an application-specific threshold (one fault in 100 clock cycles). © 2024 IEEE.

关键词： Failure rate

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Supporting Changes in Digital Ownership and Data Sovereignty Across the Automotive Value Chain with Catena-X 6

Supporting Changes in Digital Ownership and Data Sovereignty...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Manoury, Marvin Riedelsheimer, Theresa Hellmeier, Malte Meyer, Tom Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstrasse 8-9 Berlin10587 Germany Fraunhofer Institute for Software and Systems Engineering ISST Speicherstraße 6 Dortmund44147 Germany

Considering the concept of a Circular Economy, which entails several life cycles of, e.g., vehicles, their components, and materials, it is important to investigate how the respective Digital Twins are managed over the lifecycle of their physical assets. This publication presents and compares three approaches for managing Digital Twins in industrial use cases. For the aggregation of these approaches, a user-centered design approach has been used in the research project Catena-X which focuses on the automotive industry. The results are analyzed qualitatively based on aspects such as data ownership and data sovereignty. It was identified, that all three approaches have limitations and benefits which make them useful in different scenarios. An overview of their strengths and weaknesses is presented to select an appropriate approach In similar setups. The results described in this publication can be used to select the best update approach in data spaces where multiple stakeholders are able to update information on a single asset. © 2025 The Authors.

关键词： Supply chains

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Reliable Task Offloading for 6G-Based IoT Applications

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Computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Enhancing multiscale simulations with constitutive relations-aware deep operator networks

Enhancing multiscale simulations with constitutive relations...

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94th Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2024

作者： Eivazi, Hamidreza Alikhani, Mahyar Tröger, Jendrik-Alexander Wittek, Stefan Hartmann, Stefan Rausch, Andreas Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany Institute of Applied Mechanics Clausthal University of Technology Clausthal-Zellerfeld Germany

Multiscale problems are widely observed across diverse domains in physics and engineering. Translating these problems into numerical simulations and solving them using numerical schemes, for example, the finite element method, is costly due to the demand of solving initial boundary-value problems at multiple scales. On the other hand, multiscale finite element computations are commended for their ability to integrate micro-structural properties into macroscopic computational analyses using homogenization techniques. Recently, neural operator-based surrogate models have shown trustworthy performance for solving a wide range of partial differential equations. In this work, we propose a hybrid method in which we utilize deep operator networks for surrogate modeling of the microscale physics. This allows us to embed the constitutive relations of the microscale into the model architecture and to predict microscale strains and stresses based on the prescribed macroscale strain inputs. Furthermore, numerical homogenization is carried out to obtain the macroscale quantities of interest. We apply the proposed approach to quasi-static problems of solid mechanics. The results demonstrate that our constitutive relations-aware DeepONet can yield accurate solutions even when being confronted with a restricted dataset during model development. © 2024 The Author(s). Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Constitutive models

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BEHAVIOR PROXIMAL POLICY OPTIMIZATION 11

BEHAVIOR PROXIMAL POLICY OPTIMIZATION

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhuang, Zifeng Lei, Kun Liu, Jinxin Wang, Donglin Guo, Yilang Zhejiang University China School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China School of Software Engineering Beijing Jiaotong University China

Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to overestimating of out-of-distribution state-action ***, various additional augmentations are proposed to keep the learned policy close to the offline dataset (or the behavior policy).In this work, starting from the analysis of offline monotonic policy improvement, we reach a surprising conclusion that online on-policy algorithms are naturally able to solve offline ***, the inherent conservatism of these on-policy algorithms is exactly what the offline RL method needs to overcome the *** on this, we propose Behavior Proximal Policy Optimization (BPPO), which solves offline RL without any extra constraint or regularization introduced compared to *** experiments on the D4RL benchmark empirically show this extremely succinct method outperforms state-of-the-art offline RL *** implementation is available at https://***/Dragon-Zhuang/BPPO. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Color Image Steganalysis Algorithm Based on Quaternion Convolutional Neural Network 30th

Color Image Steganalysis Algorithm Based on Quaternion Convo...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Xu, Meng Wang, Jinwei State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450001 China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9783031687747

Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual design, and the steganalysis features do not fully consider the internal connections between the three channels of color images. Most steganalyzers based on deep learning are also designed for grayscale images. In recent years, the continuous introduction of advanced steganography techniques for color images has also brought more severe challenges to the steganography analysis of color images. Quaternions are a tool that can effectively represent color images, and utilizing quaternion transformations can fully exploit the correlation between color image channels. Combining quaternion theory with convolutional neural networks, this paper proposed a color image steganalysis algorithm based on quaternion convolutional neural networks. Firstly, the image is represented by quaternion, and then input into the quaternion convolutional neural network. The convolution kernel in the network is composed of quaternions, and then perform convolution and pooling. The experimental results have demonstrated the significant effectiveness of the proposed network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Distinguishing Sensitive and Insensitive Options for the Winograd Schema Challenge 28th

Distinguishing Sensitive and Insensitive Options for the ...

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28th International Conference on Database systems for advanced Applications, DASFAA 2023

作者： Li, Dong Wang, Pancheng He, Liangliang Pang, Kunyuan Li, Shasha Tang, Jintao Wang, Ting College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9783031306747

The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. We observe that the answers of many instances are insensitive to the options. In this paper, based on this observation, we propose an approach based on fine-tuning the pre-trained language model for WSC by distinguishing sensitive and insensitive options. First, we split WSC instances into option-sensitive and insensitive categories, and use option expanding and option masking strategies to weaken the options so that the model does not pay attention to options when they are insensitive during fine-tuning. Second, we treat the two categories as intermediate-task of each other, and use transfer learning to improve the performance. We fine-tune BERT-Large and T5-XXL with our approach on WINOGRANDE, a new dataset of WSC, and the experiment shows our method outperforms baselines by a large margin, achieving state-of-the-art, which indicates the effectiveness of our instance-distinguishing strategy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Analysis of Patterns in Recorded Signals of software systems With a Variance Based Segmentation Algorithm

arXiv

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arXiv 2025年

作者： Lukić, Bojan Knust, Thorben Rausch, Andreas Clausthal University of Technology Institute for Software and Systems Engineering Germany

Due to the increasing complexity and interconnectedness of different components in modern automotive software systems there is a great number of interactions between these system components and their environment. These interactions result in unique temporal behaviors we call underlying scenarios. The signal data from all system components, which is recorded during runtime, can be processed and analyzed by observing changes in their runtime. Different system behaviors can be characterized by dividing the whole data spectrum into appropriate segments with consistent behavior, classifying these segments, and mapping them to different scenarios. These disjunctive scenarios can be analyzed for their specific behavior which may divert from the expected average system behavior. We state the emerging problem of data segmentation as follows: divide a multivariate data set into a suitable amount of segments with consistent internal behavior. The problem can be divided into 2 subproblems:"How many segments are present in the data set?", and"What are good segmentation indices for the underlying segments?". The complexity of the problem still needs to be assessed, however, at this point we expect it to be NP-hard, as both the number of segments and the segmentation points are unknown. We are in search of appropriate metrics to quantify the quality of a given segmentation of a whole data set. In this paper, we discuss the segmentation of multivariate data, but not the classification of segments into scenario classes. In the following, we investigate segmentation algorithms for solving the subproblem of finding suitable segmentation indices by constant amount of segments. The algorithms are investigated towards effectivity and efficiency by applying them to a data set taken out of a real system trace provided by our automotive partners. The supplementary resources that include the data set and custom algorithms can be found in the GitHub repository1 © 2025, CC BY-SA.

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