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作者： Hahn, Ernst Moritz Hashemi, Vahid Hermanns, Holger Lahijanian, Morteza Turrini, Andrea School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom State Key Laboratory of Computer Science Institute of Software CAS Beijing China Department of Information Technology Audi AG Ingolstadt Germany Saarland University Saarland Informatics Campus Saarbrücken Germany Institute of Intelligent Software Guangzhou Guangzhou China Department of Smead Aerospace Engineering and Sciences University of Colorado BoulderCO United States

Accurate Modelling of a real-world system with probabilistic behaviour is a difficult task. Sensor noise and statistical estimations, among other imprecisions, make the exact probability values impossible to obtain. In this article, we consider Interval Markov decision processes (IMDPs), which generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. We investigate the problem of robust multi-objective synthesis for IMDPs and Pareto curve analysis of multi-objective queries on IMDPs. We study how to find a robust (randomised) strategy that satisfies multiple objectives involving rewards, reachability, and more general ω-regular properties against all possible resolutions of the transition probability uncertainties, as well as to generate an approximate Pareto curve providing an explicit view of the trade-offs between multiple objectives. We show that the multi-objective synthesis problem is PSPACE-hard and provide a value iteration-based decision algorithm to approximate the Pareto set of achievable points. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototype tool. © 2019 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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A Review of Artificial Fish Swarm Algorithms: Recent Advances and Applications

arXiv

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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 20

Bilingual Word Embedding with Sentence Combination CNN for 1...

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Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV

arXiv

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arXiv 2020年

作者： Chen, Ci Wan, Yuanfang Li, Baowei Wang, Chen Xie, Guangming Jiang, Huanyu College of Biosystems Engineering and Food Science Zhejiang University Hangzhou310058 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China Department of Ocean Science and Engineering Southern University of Science and Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China

For heterogeneous unmanned systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist UGVs in motion planning is a promising research direction due to the UAVs’ vast view scope. However, due to UAVs flight altitude limitations, it may be impossible to observe the global map, and motion planning in the local map is a POMDP (Partially Observable Markov Decision Process) problem. This paper proposes a motion planning algorithm for heterogeneous unmanned system under partial observation from UAV without reconstruction of global maps, which consists of two parts designed for perception and decision-making, respectively. For the perception part, we propose the Grid Map Generation Network (GMGN), which is used to perceive scenes from UAV’s perspective and classify the pathways and obstacles. For the decision-making part, we propose the Motion Command Generation Network (MCGN). Due to the addition of memory mechanism, MCGN has planning and reasoning abilities under partial observation from UAVs. We evaluate our proposed algorithm by comparing with baseline algorithms. The results show that our method effectively plans the motion of heterogeneous unmanned systems and achieves a relatively high success rate. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Publisher Correction: A skin-integrated multimodal haptic interface for immersive tactile feedback

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Nature Electronics 2023年第12期6卷 1041页

作者： Ya Huang Jingkun Zhou Chun Ki Yiu Kuanming Yao Dengfeng Li Jian Li Tsz Hung Wong Yiming Liu Yuyu Gao Xingcan Huang Hu Li Jiyu Li Binbin Zhang Zhenlin Chen Xinge Yu Pingchuan Ke Shaoyu Cai Xingyu Yang Haichen Gao Kening Zhu Xu Guo Zichen Zhao Zhaoqian Xie Yu Zhou Lei Li Huanxi Zheng Zuankai Wang Enming Song Hui Wu Department of Biomedical Engineering City University of Hong Kong Hong Kong China Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Hong Kong Science Park Hong Kong China Shenzhen Research Institute City University of Hong Kong Shenzhen China Department of Sociology Hong Kong Shue Yan University Hong Kong China School of Creative Media City University of Hong Kong Hong Kong China Engineering Design and Innovation Centre College of Design and Engineering National University of Singapore Singapore Singapore State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian China Department of Engineering Mechanics Dalian University of Technology Dalian China Ningbo Institute of Dalian University of Technology Ningbo China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China National Engineering Research Center of Electric Vehicles Beijing Institute of Technology Beijing China Department of Mechanical Engineering Hong Kong Polytechnic University Hong Kong China Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing China

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A flipped classroom approach for teaching a master’s course on artificial intelligence 1

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9th International Conference on Computer Supported Education, CSEDU 2017

作者： Bye, Robin T. Software and Intelligent Control Engineering Laboratory Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU—Norwegian University of Science and Technology Postboks 1517 Ålesund6025 Norway

ISBN: (数字)9783319946405

ISBN: (纸本)9783319946399

In this paper, I present a flipped classroom approach for teaching a master’s course on artificial intelligence. Traditional lectures from the classroom are outsourced to an open online course that contains high quality video lectures, step-by-step tutorials and demonstrations of intelligent algorithms, and self-tests, quizzes, and multiple-choice questions. Moreover, selected problems, or coding challenges, are cherry-picked from a suitable game-like coding development platform that rids both students and the teacher of having to implement much of the fundamental boilerplate code required to generate a suitable simulation environment in which students can implement and test their algorithms. Using the resources of the online course and the coding platform thus free up much valuable time for active learning in the classroom. These learning activities are carefully chosen to align with the intended learning outcomes, curriculum, and assessment to allow for learning to be constructed by the students themselves under guidance by the teacher. Thus, I perceive the teacher’s role as a facilitator for learning, much similar to that of a personal trainer or a coach. Emphasising problem-solving as key to achieving intended learning outcomes, the aim is to select problems that strike a balance between detailed step-by-step tutorials and highly open-ended problems. This paper consists of an overview of relevant literature, the course content and teaching methods, recent evaluation reports and a student evaluation survey, results from the final oral exams, and a discussion regarding some limiting frame factors, challenges with my approach, and future directions. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： E-learning

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Observations and reflections on teaching electrical and computer engineering courses 1

引用

9th International Conference on Computer Supported Education, CSEDU 2017

作者： Osen, Ottar L. Bye, Robin T. Software and Intelligent Control Engineering Laboratory Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU—Norwegian University of Science and Technology Postboks 1517 Ålesund6025 Norway

ISBN: (数字)9783319946405

ISBN: (纸本)9783319946399

In this article, we make a number of observations and reflections based on our experience from many years of teaching courses in electrical and computer engineering bachelor programmes. We present important aspects of attendance, lectures, group work, and compulsory coursework, and how these can be addressed to improve student learning. Moreover, we discuss how to facilitate active learning activities, focussing on simple in-classroom activities and larger problem-based activities such as assignments, projects, and laboratory work, and highlight solving real-world problems by means of practical application of relevant theory as key to achieving intended learning outcomes. Our observations and reflections are then put into a theoretical context, including students’ approaches of learning, constructive alignment, active learning, and problem-based versus problem-solving learning. Next, we present and discuss the results from two recent student evaluation surveys, one for senior (final-year) students and one for junior (first- and second-year) students, and draw some conclusions. Finally, we add some remarks regarding our findings and point to future work. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Students

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A Framework for the Integration of Coarse Sensing Information and Environmental Constraints*

A Framework for the Integration of Coarse Sensing Informatio...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Rui Li Yingbai Hu Yanjun Cao Mengyao Li School of Automation Chongqing University Chongqing China Informatics 6 Technical University of Munich Munich Germany Department of Computer and Software Engineering École Polytechnique de Montréal Boul Édouard-Montpetit Québec Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

A series of previous work has found that the environmental constraint (EC), which is the natural result of the contact of the robot and the interacting objects, is immensely helpful for the realization of high-precision robotic tasks. However, due to the existence of multifarious errors, such as mechanical error, modeling error and sensing error, there would be discrepancy between the actual constraints and the ideal models. In such case, it is hard to realize manipulation with EC-based strategies. Inspired by human, a preliminary framework which aims at the integration of the coarse sensing information and the environmental constraints is proposed for robotic manipulation. By mapping the sensing information into the new space, where the environmental constraint can be formally described, the region that integrates the sensing information and the environmental constraint is constructed and the conditions to achieve high-precision manipulation are derived. Based on the conditions, a motion planning strategy is proposed to achieve the required task. The effectiveness of this strategy is verified by case studies.

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Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

arXiv

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arXiv 2021年

作者： Ma, Lianbo Li, Nan Yu, Guo Geng, Xiaoyu Cheng, Shi Wang, Xingwei Huang, Min Jin, Yaochu College of Software Northeastern University Shenyang110819 China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing211816 China The School of Computer Science Shaanxi Normal University Xi’an710062 China The College of Computer Science Northeastern University Shenyang110819 China The College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Faculty of Technology Bielefeld University Bielefeld33619 Germany The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

In multi-objective evolutionary neural architecture search (NAS), existing predictor-based methods commonly suffer from the rank disorder issue that a candidate high-performance architecture may have a poor ranking compared with the worse architecture in terms of the trained *** alleviate the above issue, we aim to train a Pareto-wise end-to-end ranking classifier to simplify the architecture search process by transforming the complex multi-objective NAS task into a simple classification task. To this end, a classifier-based Pareto evolution approach is proposed, where an online classifier is trained to directly predict the dominance relationship between the candidate and reference architectures. Besides, an adaptive clustering method is designed to select reference architectures for the classifier, and an α-domination assisted approach is developed to address the imbalance issue of positive and negative samples. The proposed approach is compared with a number of state-of-the-art NAS methods on widely-used test datasets, and computation results show that the proposed approach is able to alleviate the rank disorder issue and outperforms other methods. Especially, the proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints. © 2021, CC BY.

关键词： Network architecture

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Functional Synthetic Biology

arXiv

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arXiv 2022年

作者： Aldulijan, Ibrahim Beal, Jacob Billerbeck, Sonja Bouffard, Jeff Chambonnier, Gaël Delkis, Nikolaos Guerreiro, Isaac Holub, Martin Ross, Paul Selvarajah, Vinoo Sprent, Noah Vidal, Gonzalo Vignoni, Alejandro Systems Engineering Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States Intelligent Software & Systems Raytheon BBN Technologies 10 Moulton Street CambridgeMA02138 United States Molecular Microbiology Groningen Biomolecular Sciences and Biotechnology Institute University of Groningen Nijenborgh 7 Groningen9747 AG Netherlands Centre for Applied Synthetic Biology Department of Biology Concordia University 7141 Sherbrooke Street West MontréalQCH4B 1R6 Canada Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Plant and Environmental Biotechnology Laboratory Department of Biochemistry and Biotechnology University of Thessaly Viopolis Mezourlo Larissa41500 Greece iGEM Foundation 45 Prospect Street CambridgeMA02139 United States Delft University of Technology Van der Maasweg 9 2629 HZ Netherlands BioStrat Marketing 9965 Harbour Lake Circle Boynton BeachFL33437 United States Department of Chemical Engineering Imperial College London South Kensington Campus Exhibition Road SW7 2AZ United Kingdom Research Group School of Computing Newcastle University Devonshire Building Devonshire Terrace Newcastle Upon TyneNE1 7RU United Kingdom Synthetic Biology and Biosystems Control Lab Instituto de Automatica e Informatica Industrial Universitat Politecnica de Valencia Camino de Vera s/n Valencia46022 Spain

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility, and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability, and reductions in technical risk and cost. © 2022, CC BY.

关键词： Biological systems

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