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IEEE International Joint Conference on Biometrics (IJCB)

作者： Bo Peng Xianyun Sun Caiyong Wang Wei Wang Jing Dong Zhenan Sun Rongyu Zhang Heng Cong Lingzhi Fu Hao Wang Yusheng Zhang Hanyuan Zhang Xin Zhang Boyuan Liu Hefei Ling Luka Dragar Borut Batagelj Peter Peer Vitomir Štruc Xinghui Zhou Kunlin Liu Weitao Feng Weiming Zhang Haitao Wang Wenxiu Diao Center for Research on Intelligent Perception and Computing (CRIPAC) Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China Beijing University of Civil Engineering and Architecture (BUCEA) China Interactive Entertainment Group of Netease Inc Guangzhou China Huazhong University of Science and Technology (HUST) China Faculty of Computer and Information Science University of Ljubljana (UNILJ) Slovenia Faculty of Electrical Engineering University of Ljubljana (UNILJ) Slovenia University of Science and Technology of China (USTC) China Institute of Network Technology (INT) Yantai China Nanjing University of Science and Technology (NUST) China

This paper presents the summary report on the DeepFake Game Competition on Visual Realism Assessment (DFGC-VRA). Deep-learning based face-swap videos, also known as deepfakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. There is a ongoing deepfake game between its creators and detectors, with the human in the loop. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also useful as a quality metric to compare different face-swap methods. This is the third edition of DFGC competitions, which focuses on the new visual realism assessment topic, different from previous ones that compete creators versus detectors. With this competition, we conduct a comprehensive study of the SOTA performance on the new task. We also release our MindSpore codes to further facilitate research in this field (https://***/bomb2peng/DFGC-VRA-benckmark).

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CSI-Based Human Activity Recognition using Convolutional Neural Networks 11

CSI-Based Human Activity Recognition using Convolutional Neu...

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11th International Conference on Computer Engineering and Knowledge, ICCKE 2021

作者： Moshiri, Parisa Fard Nabati, Mohammad Shahbazian, Reza Ghorashi, Seyed Ali Shahid Beheshti University G. C. Cognitive Telecommunication Research Group Department of Electrical Engineering Tehran Iran Standard Research Institute Faculty of Technology and Engineering Department of Electrical Engineering Alborz31745-139 Iran University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9781665402088

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as "walk", "run", and "sleep". These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks. © 2021 IEEE.

关键词： Channel state information

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Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

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Neural computing and Applications 2024年第23期36卷 14119-14150页

作者： Bhagat, Suraj Kumar Tiyasha, Tiyasha Shather, A.H. Jamei, Mehdi Kumar, Adarsh Al-Khafaji, Zainab Goliatt, Leonardo Shafik, Shafik S. Alawi, Omer A. Yaseen, Zaher Mundher Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Engineering Technology Al Kitab University Altun Kopru Kirkuk00964 Iraq Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St PetersPE Canada Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Hillah Babylon51001 Iraq Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora Brazil Experimental Nuclear Radiation Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Johor Bahru Skudai81310 Malaysia Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Dept of Environmental Science Gitam School of Science GITAM University Visakhapatnam India

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi;and for minimum number of inputs i.e four, are selected for PhuongHung, CanTho, and SaPa city;that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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CSI-Based Human Activity Recognition using Convolutional Neural Networks

CSI-Based Human Activity Recognition using Convolutional Neu...

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International eConference on Computer and Knowledge Engineering (ICCKE)

作者： Parisa Fard Moshiri Mohammad Nabati Reza Shahbazian Seyed Ali Ghorashi Cognitive Telecommunication Research Group Shahid Beheshti University G. C. Tehran Iran Faculty of Technology and Engineering Standard Research Institute Alborz Iran School of Architecture Computing and Engineering University of East London London UK

ISBN: (纸本)9781665402095

关键词： Performance evaluation Smart cities Transportation Activity recognition Robot sensing systems Convolutional neural networks Task analysis

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The Hitchhiker's Guide to Fused Twins: A Review of Access to Digital Twins in situ in Smart Cities

arXiv

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arXiv 2022年

作者： Grübel, Jascha Thrash, Tyler Aguilar, Leonel Gath-Morad, Michal Chatain, Julia Sumner, Robert W. Hölscher, Christoph Schinazi, Victor R. Cognitive Science ETH Zurich Switzerland Game Technology Center ETH Zurich Switzerland Visual Computing Group Harvard University United States Center for Sustainable Future Mobility ETH Zurich Switzerland Geoinformation Engineering ETH Zurich Switzerland Department of Biology Saint Louis University United States Data Science Systems and Services Group ETH Zurich Switzerland Department of Architecture University of Cambridge United Kingdom Bartlett School of Architecture University College London United Kingdom Professorship for Learning Sciences and Higher Education ETH Zurich Switzerland Department of Psychology Bond University Australia

Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (Physical, Data, Analytical, Virtual, and Connection environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions. © 2022, CC BY.

关键词： Sensor networks

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Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

arXiv

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arXiv 2022年

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Switzerland AI Witchlabs Switzerland University of Wuerzburg Germany MediaTek Inc. Taiwan National Yang Ming Chiao Tung University Taiwan Video Algorithm Group Camera Department Xiaomi Inc. China Audio & Video Technology Platform Department ZTE Corp. China China School of Computer Science and Technology Xi'an Jiaotong University China MIGU Video Co. Ltd China BOE Technology Group Co. Ltd. China GenGenAI Korea Republic of North China University of Technology China Institute of Automation Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Sweden Northeastern University China

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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Correction: RILEM TC 277-LHS report: How hot are hot-lime-mixed mortars? A review

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Materials and Structures 2023年第7期56卷 1-2页

作者： Pavia, S. Veiga, R. Hughes, J. Pesce, G. Valek, J. Alvarez, J. I. Faria, P. Padovnik, A. Department of Civil Engineering Trinity College Dublin Dublin Ireland Buildings Department National Laboratory for Civil Engineering–LNEC Lisbon Portugal School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley Scotland UK Department of Architecture and Built Environment Northumbria University Newcastle Upon Tyne UK Institute of Theoretical and Applied Mechanics Academy of Sciences of the Czech Republic Prague 9 Czech Republic Materials and Cultural Heritage MATCH Research Group Department of Chemistry School of Sciences University of Navarra Pamplona Spain CERIS and Department of Civil Engineering NOVA School of Science and Technology Universidade NOVA de Lisboa Caparica Portugal Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia

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Responsible urban innovation with local government artificial intelligence (Ai): A conceptual framework and research agenda

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Journal of Open Innovation: technology, Market, and Complexity 2021年第1期7卷 1-16页

作者： Yigitcanlar, Tan Corchado, Juan M. Mehmood, Rashid Li, Rita Yi Man Mossberger, Karen Desouza, Kevin School of Architecture and Built Environment Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia School of Technology Federal University of Santa Catarina Campus Universitario Trindade Florianópolis SC 88040-900 Brazil Bisite Research Group University of Salamanca Salamanca 37007 Spain Air Institute IoT Digital Innovation Hub Salamanca 37188 Spain Department of Electronics Information and Communication Faculty of Engineering Osaka Institute of Technology Osaka 535-8585 Japan High Performance Computing Center King Abdulaziz University Al Ehtifalat St Jeddah 21589 Saudi Arabia Department of Economics and Finance Hong Kong Shue Yan University 10 Wai Tsui Cres North Point Hong Kong School of Public Affairs Arizona State University 411 North Central Avenue Phoenix 85004 AZ United States School of Management Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia

The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development;where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： AI revolution Artificial intelligence (AI) Community engagement Local government AI Responsible AI Responsible innovation Responsible urban innovation Smart city Urban policy Urban technology

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Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

arXiv

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arXiv 2023年

作者： Bousbiat, Hafsa Bousselidj, Roumaysa Himeur, Yassine Amira, Abbes Bensaali, Faycal Fadli, Fodil Mansoor, Wathiq Elmenreich, Wilfried University of Applied Sciences Institut für Informatic Wiener Neustadt2700 Austria University of Skikda Department of Computer Science Mezghich Skikda21000 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates De Montfort University Leicester Institute of Artificial Intelligence United Kingdom Qatar University Department of Electrical Engineering Doha2713 Qatar Qatar University College of Engineering Department of Architecture and Urban Planning Doha2713 Qatar University of Klagenfurt Smart Grids Research Group Klagenfurt9020 Austria

Consumer’s privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented. © 2023, CC BY-NC-SA.

关键词： Internet of Energy

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Investigating order information in API-usage patterns: A benchmark and empirical study 13

Investigating order information in API-usage patterns: A ben...

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13th International Conference on Software Technologies, ICSOFT 2018

作者： Çergani, Ervina Proksch, Sebastian Nadi, Sarah Mezini, Mira Software Technology Group Technische Universität Darmstadt Darmstadt Germany Software Evolution and Architecture Lab University of Zürich Zürich Switzerland Department of Computing Science University of Alberta AB Canada

ISBN: (纸本)9789897583209

Many approaches have been proposed for learning Application Programming Interface (API) usage patterns from code repositories. Depending on the underlying technique, the mined patterns may (1) be strictly sequential, (2) consider partial order between method calls, or (3) not consider order information. Understanding the trade-offs between these pattern types with respect to real code is important in many applications (e.g. code recommendation or misuse detection). In this work, we present a benchmark consisting of an episode mining algorithm that can be configured to learn all three types of patterns mentioned above. Running our benchmark on an existing dataset of 360 C# code repositories, we empirically study the resulting API usage patterns per pattern type. Our results show practical evidence that not only do partial-order patterns represent a generalized super set of sequential-order patterns, partial-order mining also finds additional patterns missed by sequence mining, which are used by a larger number of developers across code repositories. Additionally, our study empirically quantifies the importance of the order information encoded in sequential and partial-order patterns for representing correct co-occurrences of code elements in real code. Furthermore, our benchmark can be used by other researchers to explore additional properties of API patterns. Copyright © 2018 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved

关键词： Economic and social effects

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