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17th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling Forum, 1st International workshop on Models for Simulation, M4S 2024, 2nd International workshop on the Foundations and Applications of Capabilities in Enterprises, Transformations, and ESG Initiatives, FACETE 2024, workshop on Advancing Enterprise Modelling through Digital Transformation, FAIR Data Management, and Blockchain Integration, AEM 2024, Tools and Demos, PoEM-Companion 2024

作者： Siljefors, Andreas Hacks, Simon Stockholm University Stockholm Sweden

the rapid evolution of software systems often necessitates corresponding changes in their underlying models. However, ensuring these models remain consistent with their evolving meta-models is a complex challenge within Model-Driven engineering (MDE). this work addresses this challenge by adapting and implementing a model co-evolution approach within the Meta Attack Language (MAL), a domain-specific language for modeling cyber threats. the primary goal of this research is to develop a prototype system that can automatically generate migration strategies to restore or improve the conformance between models and their meta-models following meta-model changes. It utilizes a multi-objective evolutionary algorithm (NSGA-II) to explore the search space for possible edit operations. the system was designed to prioritize correctness and reduce manual effort while accommodating requirements drawn from a design process backed by a conceptual framework developed from subsequent literature. A series of controlled experiments evaluated the prototype system against several models derived from the MAL language coreLang. the results demonstrate that the system does provide some effort in reestablishing model conformance, although with some performance variability. Our work contributes to the field of MDE by providing insights into the practical application of model co-evolution techniques in a domain-specific context. the developed prototype is a foundation for future research to refine these techniques and expand their applicability to a broader range of modeling scenarios. © 2024 Copyright for this paper by its authors.

关键词： Domain Specific Languages Meta Attack Language Model Co-evolution Model-Driven engineering

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Optimization algorithm Research and Design of Chip Manufacturing Virtual System Based on SECS/GEM

Optimization Algorithm Research and Design of Chip Manufactu...

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IEEE International workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Ke Liu Kairong Zhao Yaming Gong Shan He Donghui Guo School of Electronic Science and Engineering Xiamen University Xiamen China

In order to improve the production efficiency, improve the product quality and reduce the cost in the field of chip manufacturing, this paper designs a virtual system based on SECS/GEM protocol to simulate the real production process of chip manufacturing, so as to optimize each stage of chip manufacturing. the system includes a equipment-side program that simulates the equipment and a host-side program that interacts with it. the host-side can collect state data from the equipment and monitor the equipment. Secondly, in order to improve production efficiency, an optimization algorithm of job shop scheduling based on reinforcement deep learning is designed. experiments show that the system can simulate the process of chip manufacturing to some extent, optimize and improve the manufacturing process, and improve the overall efficiency of the equipment.

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Hybrid Whale Optimization algorithm for Solving Green Open Vehicle Routing Problem with Time Windows 17th

Hybrid Whale Optimization Algorithm for Solving Green Open V...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Jiang, Wen Hu, Rong Qian, Bin Yu, Nai-Kang Liu, Bo School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Academy of Mathematics and Systems Sciences Chinese Academy of Sciences Beijing10084 China

ISBN: (纸本)9783030845216

Based on the perspective of energy efficiency and minimizing fuel consumption as the optimization goal, this paper establishes a Green Open Vehicle Routing Problem (GOVRP) model, and designs a Hybrid Whale Optimization algorithm (HWOA). To solve. HWOA integrates the specific operations in the mechanism on the premise that the individual update mechanism in the standard WOA remains unchanged, so that it can quickly perform the search operation of the solution space. A local search operation based on 4 kinds of neighborhood operations is designed to perform a more detailed search in the high-quality solution domain. through the simulation experiments and algorithm comparison of several test cases with different customer sizes, it is verified that HWOA can effectively solve GOVRP. © 2021, Springer Nature Switzerland AG.

关键词： Energy efficiency

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ERA: Extracting Planning Macro-Operators from Adjacent and Non-adjacent Sequences 1

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17th Pacific Rim Knowledge Acquisition workshop, PKAW 2020 held in conjunction with the International Joint Conference on Artificial Intelligence - Pacific Rim International Conference on Artificial Intelligence, IJCAI-PRICAI 2020

作者： Castellanos-Paez, Sandra Rombourg, Romain Lalanda, Philippe Univ. Grenoble Alpes CNRS Grenoble INP Institute of Engineering Univ. Grenoble Alpes LIG Grenoble38000 France

ISBN: (数字)9783030698867

ISBN: (纸本)9783030698850

Intuitively, Automated Planning systems capable of learning from previous experiences should be able to achieve better performance. One way to build on past experiences is to augment domains with macro-operators (i.e. frequent operator sequences). In most existing works, macros are generated from chunks of adjacent operators extracted from a set of plans. Although they provide some interesting results this type of analysis may provide incomplete results. In this paper, we propose ERA, an automatic extraction method for macro-operators from a set of solution plans. Our algorithm is domain and planner independent and can find all macro-operator occurrences even if the operators are non-adjacent. Our method has proven to successfully find macro-operators of different lengths for six different benchmark domains. Also, our experiments highlighted the capital role of considering non-adjacent occurrences in the extraction of macro-operators. © 2020, Springer Nature Switzerland AG.

关键词： Data mining

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Recognition and Filtering of Tremor Signals for Vascular Interventional Surgical Robot 17

Recognition and Filtering of Tremor Signals for Vascular Int...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Guo, Shuxiang Zhang, Liuqing Yang, Cheng Shen, Rui Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology School of Automation No.5 Zhongguancun South Street Haidian District Beijing100081 China

ISBN: (纸本)9781728164151

During the operation of vascular intervention surgery, the surgeon may cause an involuntary tremor of hand due to a long time operation. However, during the actual surgical operation, when the surgeon performs complicated operations on same vascular area (such as selecting a blood vessel and pushing the catheter/guide wire into the thrombus), he needs to actively perform tremor to achieve the catheter/guidewire tremor in order to pass the catheter/guidewire. It is difficult to push or select the part of the blood vessel. this article is to eliminate the tremor of the operator's hand, and accurately identify the tremor signal in the signal through a support vector machine (SVM). the signal is filtered after identification, so that the signal received by the manipulator arm during the operation of the guide wire of the surgical robot does not contain the tremor signal this paper verifies the performance of tremor suppression algorithm design experiments and evaluates the algorithm as a whole. the results show that the algorithm has good tremor recognition and suppression effect. © 2020 IEEE.

关键词： Support vector machines

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One Class Support Vector Machine Active Learning Method for Unbalanced Data 12

One Class Support Vector Machine Active Learning Method for ...

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12th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2020

作者： Dai, Jin Liu, Haichuan Zhang, Qirui School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781728184487

Active learning is an important method to efficiently reduce the labeled sample size in large-scale machine learning. As the large amount of data presents the nature of imbalance, the study of active learning methods for corresponding unbalanced datasets highlights important practical significance. there is a problem with the current algorithm. the initialization of active learning becomes difficult with the increase of the unbalanced degree, which causes more labels waste. this paper proposes a one-class support vector machine active learning (OCSVM-AL) method for this problem. Firstly, we obtain the structure information of the sample set by k-means clustering. then, the OCSVM-AL is used on each cluster. For each cluster, OCSVM-AL iteratively marks the sample from the cluster and adds the marked sample to the marked sample pool. A one-class support vector machine model is trained by using majority class samples in the marked sample pool. the trained model is used to predict the unlabeled samples in this cluster, and the labels of unlabeled samples is obtained. By the results of each cluster, all sample labels are obtained. Simulation experiments proved that this method is an effective active learning method and can significantly reduce the labeled sample size. © 2020 IEEE.

关键词： Support vector machines

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engineering a delegatable and error-Tolerant algorithm for counting small subgraphs 20

Engineering a delegatable and error-Tolerant algorithm for c...

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20th workshop on algorithm engineering and experiments, alenex 2018

作者： Kaski, Petteri Department of Computer Science Aalto University Helsinki Finland

ISBN: (纸本)9781611975055

We study the problem of counting the number of occurrences of a given six-vertex pattern graph S in an n-vertex host graph H. We engineer an open-source GPU implementation of a distributed algorithm design of Björklund and Kaski [PODC 2016] where (i) the execution of the algorithm can be delegated [Goldwasser, Kalai, and Rothblum, J. ACM 2015] to produce a noninteractive probabilistically checkable proof of correctness, and (ii) the execution of the algorithm when preparing the proof tolerates a controllable number of adversarial errors. experiments with NVIDIA Tesla K80 and Tesla P100 Accelerators demonstrate that the framework is practical for inputs of up to 512 vertices, with proof checking being several orders of magnitude more efficient than preparing the proof;however, proof preparation still carries at least one order of magnitude overhead compared with just solving the problem. Copyright © 2018 by SIAM.

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A Reference-Inspired Evolutionary algorithm with Subregion Decomposition for Many-Objective Optimization

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2018年 650卷 145-156页

作者： Fu, Xiaogang Sun, Jianyong Zhang, Qingfu School of Electronic Engineering Shanghai Dianji University PuDong Shanghai201306 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9783319669380

In this paper, we propose a reference-inspired multiobjective evolutionary algorithm for many-objective optimisation. the main idea is (1) to summarise information inspired by a set of randomly generated reference points in the objective space to strengthen the selection pressure towards the Pareto front;and (2) to decompose the objective space into subregions for diversity management and offspring recombination. We showed that the mutual relationship between the objective vectors and the reference points provides not only a fine selection pressure, but also a balanced convergence-diversity information. the decomposition of the objective space into subregions is able to preserve the Pareto front’s diversity. A restricted stable match strategy is proposed to choose appropriate parent solutions from solution sets constructed at the subregions for high-quality offspring generation. Controlled experiments conducted on a commonly-used benchmark test suite have shown the effectiveness and competitiveness of the proposed algorithm in comparison with several state-of-the-art many-objective evolutionary algorithms. © 2018, Springer International Publishing AG.

关键词： Evolutionary algorithms

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Fast spaced seed hashing 17

Fast spaced seed hashing

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17th International workshop on algorithms in Bioinformatics, WABI 2017

作者： Girotto, Samuele Comin, Matteo Pizzi, Cinzia Department of Information Engineering University of Padova Padova Italy

ISBN: (纸本)9783959770507

Hashing k-mers is a common function across many bioinformatics applications and it is widely used for indexing, querying and rapid similarity search. Recently, spaced seeds, a special type of pattern that accounts for errors or mutations, are routinely used instead of k-mers. Spaced seeds allow to improve the sensitivity, with respect to k-mers, in many applications, however the hashing of spaced seeds increases substantially the computational time. Hence, the ability to speed up hashing operations of spaced seeds would have a major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient. In this paper we address the problem of efficient spaced seed hashing. the proposed algorithm exploits the similarity of adjacent spaced seed hash values in an input sequence in order to efficiently compute the next hash. We report a series of experiments on NGS reads hashing using several spaced seeds. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6x to 5.3x, depending on the structure of the spaced seed. © Samuele Girotto, Matteo Comin, and Cinzia Pizzi.

关键词： Bioinformatics

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Sparsification enables predicting kissing hairpin pseudoknot structures of long RNAs in practice 17

Sparsification enables predicting kissing hairpin pseudoknot...

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17th International workshop on algorithms in Bioinformatics, WABI 2017

作者： Jabbari, Hosna Wark, Ian Montemagno, Carlo Will, Sebastian Ingenuity Lab Department of Chemical and Materials Engineering University of Alberta EdmontonAB Canada Institute for Theoretical Chemistry University of Vienna Vienna Austria

ISBN: (纸本)9783959770507

While computational RNA secondary structure prediction is an important tool in RNA research, it is still fundamentally limited to pseudoknot-free structures (or at best very simple pseudoknots) in practice. Here, we make the prediction of complex pseudoknots - including kissing hairpin structures - practically applicable by reducing the originally high space consumption. For this aim, we apply the technique of sparsification and other space-saving modifications to the recurrences of the pseudoknot prediction algorithm by Chen, Condon and Jabbari (CCJ algorithm). thus, the theoretical space complexity of free energy minimization is reduced to -(n3 + Z), in the sequence length n and the number of non-optimally decomposable fragments ("candidates") Z. the sparsified CCJ algorithm, sparseCCJ, is presented in detail. Moreover, we provide and compare three generations of CCJ implementations, which continuously improve the space requirements: the original CCJ implementation, our first modified implementation, and our final sparsified implementation. the two latest implementations implement the established HotKnots DP09 energy model. In our experiments, using 244GB of RAM, the original CCJ implementation failed to handle sequences longer than 195 bases;sparseCCJ handles our pseudoknot data set (up to about length 400 bases) in this space limit. All three CCJ implementations are available at https://***/HosnaJabbari/CCJ. © Hosna Jabbari, Ian Wark, Carlo Montemagno, and Sebastian Will.

关键词： RNA

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