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Integrating a Digital Twin Concept in the Zero Emission Sea Transporter (ZEST) Project for Sustainable Maritime Transport using Stonefish Simulator

arXiv

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arXiv 2024年

作者： Grimaldi, Michele Cernicchiaro, Carlo Rossides, George Ktoris, Angelos Yfantis, Elias Kyriakides, Ioannis Computer Vision and Robotics Institute University of Girona Girona Spain School of Engineering & Physical Sciences Heriot-Watt University United Kingdom Marine Robotics Innovation Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine & Offshore Science Technology & Engineering Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine Technology Division Cyprus Marine and Maritime Institute Larnaca Cyprus Computational Cognition Lab Open University of Cyprus Nicosia Cyprus Department of Engineering University of Nicosia Nicosia Cyprus

In response to stringent emission reduction targets imposed by theInternational Maritime Organization (IMO) and the European Green Deal\'s Fit for55 legislation package, the maritime industry has shifted its focus towardsdecarbonization. While significant attention has been placed on vesselsexceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping,amounting to approximately 13% of global shipping transportation and 15% withinthe European Union (EU), have not received adequate consideration. Thisabstract introduces the Zero Emission Sea Transporter (ZEST) project, designedto address this issue by developing a zero-emissions multi-purpose catamaranfor short sea route © 2024, CC BY.

关键词： Emission control

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Neural Correlates of Perceiving Animacy in Robotic Objects

Research Square

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Research Square 2024年

作者： Yizhar, Or Maimon, Amber Tal, Zohar Wald, Iddo Yehoshua Erel, Hadas Friedman, Doron Zuckerman, Oren Amedi, Amir The Baruch Ivcher Institute for Brain Cognition and Technology Baruch Ivcher School of Psychology Reichman University Herzliya Israel Max Planck Institute for Human Development Research Group Adaptive Memory and Decision Making Germany Berlin Germany Computational Psychiatry and Neurotechnology Lab Department of Brain and Cognitive Sciences Ben Gurion University Be’er Sheva Israel Digital Media Lab Department of Mathematics and Computer Science University of Bremen Bremen Germany Proaction Laboratory Faculty of Psychology and Educational Sciences University of Coimbra Coimbra Portugal Media Innovation Lab Sammy Ofer School of Communications Reichman University Herzliya Israel Advanced Reality Lab Sammy Ofer School of Communications Reichman University Herzliya Israel

The prevalence of robots in modern society calls for gaining a fundamental understanding of the underlying principles of human-robot interaction. In particular, we need to understand how people perceive robots and how different physical and social characteristics affect this perception. Here, we examined the neural correlates of observing simple gestures performed by a non-humanoid social robot, focusing on the role of the Ventral Occipitotemporal (VOT) cortex in processing perceived animacy. Our results reveal that a broad bilateral cortical network is activated, comprising areas related to action observation, the social brain, and the visual cortex, including the VOT. We also found a correlation between the level of neural responses in the VOT, especially in the left hemisphere, and individual animacy ratings of the robotic object. This correlation provides a physical basis for such subjective ratings. These results suggest a complex representation of perceived animacy across the lateral-medial axis of the VOT, explained by spatial cortical location and the amplitude of neural responses. Furthermore, we discuss the implications of these findings for understanding how people perceive robots, their behaviors and intentions, which are crucial for developing social robots that can interact with humans more naturally. © 2024, CC BY.

关键词： Human robot interaction

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Computational Methods for Prediction of Human Protein-Phenotype Associations:A Review

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Phenomics 2021年第4期1卷 171-185页

作者： Lizhi Liu Shanfeng Zhu School of Computer Science Fudan UniversityShanghai 200433China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghai 200433China MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Zhangjiang Fudan International Innovation Center Shanghai 200433China Shanghai Key Lab of Intelligent Information Processing Fudan UniversityShanghai 200433China

Deciphering the relationship between human proteins(genes)and phenotypes is one of the fundamental tasks in phenomics *** Human Phenotype Ontology(HPO)builds upon a standardized logical vocabulary to describe the abnormal phenotypes encountered in human diseases and paves the way towards the computational analysis of their genetic *** date,many computational methods have been proposed to predict the HPO annotations of *** this paper,we conduct a comprehensive review of the existing approaches to predicting HPO annotations of novel proteins,identifying missing HPO annotations,and prioritizing candidate proteins with respect to a certain HPO *** each topic,we first give the formalized description of the problem,and then systematically revisit the published literatures highlighting their advantages and disadvantages,followed by the discussion on the challenges and promising future *** addition,we point out several potential topics to be worthy of exploration including the selection of negative HPO annotations and detecting HPO *** believe that this review will provide insight to the researchers in the field of computational phenotype analyses in terms of comprehending and developing novel prediction algorithms.

关键词： Human Phenotype Ontology(HPO) Human protein-phenotype association HPO annotation Machine learning Deep learning

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ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Inten...

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IEEE International Conference on Big Data

作者： Haochuan Cui Junjie Sheng Bo Jin Yiqiu Hu Li Su Lei Zhu Wenli Zhou Xiangfeng Wang School of Computer Science and Technology East China Normal University Shanghai China Algorithm Innovation Lab. Huawei Cloud Computing Technologies Co. Xi’an China Computing Service Product Dept. Huawei Cloud Computing Technologies Co. Xi’an China

ISBN: (纸本)9781665480468

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei Cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods.

关键词： Cloud computing Scheduling algorithms Big Data Virtual machining Planning

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Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal Knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

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An intelligent file transfer optimization for poor network conditions 6th

An intelligent file transfer optimization for poor network c...

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6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Yan, Ming Zhang, Bo Zhao, Gang Kuang, Xiaohui Liu, Lu Zhang, Ruyun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Information Center of Ministry of Human Resources and Social Security Beijing100090 China National Key Laboratory of Science and Technology on Information System Security Beijing100091 China Zhejiang Lab HangzhouZhejiang Province China

ISBN: (纸本)9789811590306

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate. © Springer Nature Singapore Pte Ltd 2020.

关键词： Transmission control protocol

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Quantifying the Dynamics of Harm Caused by Retracted Research

arXiv

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arXiv 2024年

作者： Huang, Yunyou Zhao, Jiahui Cui, Dandan Yang, Zhengxin Xia, Bingjie Liang, Qi Liu, Wenjing Ma, Li Tang, Suqin Hao, Tianyong Zhang, Zhifei Gao, Wanling Zhan, Jianfeng Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science South China Normal University Guangzhou510631 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China Department of Physiology and Pathophysiology Capital Medical University Beijing100069 China The second Affiliated hospital of Guilin medical university Guilin541004 China Guilin Medical University Guilin541100 China XuanJi Technology Co. Ltd. Guilin541012 China University of Chinese Academy of Sciences Beijing100086 China The International Open Benchmark Council Delaware19801 United States

Despite enormous efforts devoted to understanding the characteristics and impacts of retracted papers, little is known about the mechanisms underlying the dynamics of their harm and the dynamics of its propagation. Here, we propose a citation-based framework to quantify the harm caused by retracted papers, aiming to uncover why their harm persists and spreads so widely. We uncover an "attention escape" mechanism, wherein retracted papers postpone significant harm, more prominently affect indirectly citing papers, and inflict greater harm on citations in journals with an impact factor less than 10. This mechanism allows retracted papers to inflict harm outside the attention of authors and publishers, thereby evading their intervention. This study deepens understanding of the harm caused by retracted papers, emphasizes the need to activate and enhance the attention of authors and publishers, and offers new insights and a foundation for strategies to mitigate their harm and prevent its spread. Copyright © 2024, The Authors. All rights reserved.

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Structured probabilistic end-to-end learning from crowds 29

Structured probabilistic end-to-end learning from crowds

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Chen, Zhijun Wang, Huimin Sun, Hailong Chen, Pengpeng Han, Tao Liu, Xudong Yang, Jie SKLSDE Lab School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Web Information Systems Delft University of Technology Netherlands

ISBN: (纸本)9780999241165

End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

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Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony 4th

Potential Indicator for Continuous Emotion Arousal by Dyna...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Pan, Guandong Wu, Zhaobang Yang, Yaqian Wang, Xin Liu, Longzhao Zheng, Zhiming Tang, Shaoting School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China

ISBN: (纸本)9789819640003

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitable for classifying emotions-first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method’s reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography (EEG) Emotion Annotation Inter-subject Correlation

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Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization

arXiv

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arXiv 2022年

作者： Yu, Jiashuo Pu, Junfu Cheng, Ying Feng, Rui Shan, Ying The School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai200438 China The Academy for Engineering and Technology Fudan University Shanghai200438 China The Applied Research Center PCG Tencent Shenzhen518000 China

Although audio-visual representation has been proven to be applicable in many downstream tasks, the representation of dancing videos, which is more specific and always accompanied by music with complex auditory contents, remains challenging and uninvestigated. Considering the intrinsic alignment between the cadent movement of the dancer and music rhythm, we introduce MuDaR, a novel Music-Dance Representation learning framework to perform the synchronization of music and dance rhythms both in explicit and implicit ways. Specifically, we derive the dance rhythms based on visual appearance and motion cues inspired by the music rhythm analysis. Then the visual rhythms are temporally aligned with the music counterparts, which are extracted by the amplitude of sound intensity. Meanwhile, we exploit the implicit coherence of rhythms implied in audio and visual streams by contrastive learning. The model learns the joint embedding by predicting the temporal consistency between audio-visual pairs. The music-dance representation, together with the capability of detecting audio and visual rhythms, can further be applied to three downstream tasks: (a) dance classification, (b) music-dance retrieval, and (c) music-dance retargeting. Extensive experiments demonstrate that our proposed framework outperforms other self-supervised methods by a large margin. © 2022, CC BY.

关键词： Supervised learning

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