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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Daker, Mahmoud Elsayaad, Farida Atia, Ayman Faculty of Computer Science Giza Egypt Faculty of Computers and Artificial Intelligence Helwan University HCI-LAB Egypt

ISBN: (纸本)9798350381030

This paper aims to shed light on the efficiency of using LSTM in the binary classification of Karate Kata players' styles. In addition, this paper showcases the results of the proposed LSTM model on the MPII Human Pose Dataset. Since our focus in this research is LSTMs, our paper also showcases the different approaches we applied in order to reach the best classification results. Moreover, the approach we settled on was to classify each video as a whole, using a player's landmarks as input for the LSTM model. This study concludes that in comparison to segmenting the video and classifying the moves individually, the approach of classifying the video as a whole showed significant improvement. It was also concluded that using LSTMs was efficient in the task of classifying the Karate Kata style of the player with an accuracy of 90.3%. Furthermore, the model's precision, recall, and F1-Score were 94%, 90%, and 92% respectively. It is also worth mentioning that different approaches such as using classical machine learning models also showed promising results. Further research in the field is needed in order to reach a more concise result. © 2023 IEEE.

关键词： Long short-term memory

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Karate First Kata Performance Analysis and Evaluation with computer Vision and Machine Learning 3

Karate First Kata Performance Analysis and Evaluation with C...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Elkess, George Elmoushy, Seif Atia, Ayman Faculty of Computer Science Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350306231

Karate kata is a martial art that involves performing a sequence of movements with accuracy and speed. In this paper, we present a system capable of automatically recognizing and evaluating karate performances using computer vision and machine learning. Our system comprises four main steps: pose estimation, movement detection, feature extraction, and classification. We utilize the ensembling of two models and 14 different binary classification models in the classification step. We have successfully developed a method that compensates for camera shake in movement detection within our karate performance recognition system. Subsequently, we compute features such as skew, skewness, kurtosis, and covariance. Lastly, a machine learning model is employed to classify the first karate kata video. We curate a dataset of karate kata videos and achieve an impressive accuracy of 91.99% in our classification task. © 2023 IEEE.

关键词： computer vision

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Interpreting Black-Box Time Series Classifiers using Parameterised Event Primitives 2

Interpreting Black-Box Time Series Classifiers using Paramet...

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Joint of the 2nd World Conference on eXplainable artificial intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Mekonnen, Ephrem T. Longo, Luca Dondio, Pierpaolo School of Computer Science Technological University Dublin Ireland Artificial Intelligence and Cognitive Load Research Lab. Technological University Dublin Ireland

Amidst the remarkable performance of deep learning models in time series classification, there is a pressing demand for methods that unveil their prediction rationale. Existing feature importance techniques often neglect the temporal nature of time series data, focusing solely on segment importance. Addressing this gap, this paper introduces a local model-agnostic method akin to LIME, which generates neighbouring samples by randomly perturbing segments of the original instance. Subsequently, weights are computed for each neighbouring instance based on its distance from the original, elucidating its influence. Parameterised event primitives (PEPs) are then extracted from these perturbed samples, encompassing increasing and decreasing events and local maxima and minima points. These primitives are clustered to form prototypical events that capture the temporal essence of the data. Leveraging these events, computed weights, and black box predictions, a simple linear regression model is trained to provide local explanations. Preliminary experiments on real-world datasets showcase the method’s efficacy in identifying salient subsequences and points and their importance scores, thereby enhancing comprehension of produced explanations. © 2022 Copyright for this paper by its authors.

关键词： Spatio-temporal data

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Natural Image Matting with Attended Global Context

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Journal of computer science & Technology 2023年第3期38卷 659-673页

作者：张億一牛力 Yasushi Makihara 张健夫赵维杰 Yasushi Yagi 张丽清 MoE Key Lab of Artificial Intelligence Department of Computer Science and EngineeringShanghai Jiao Tong University Shanghai 200240China Department of Intelligent Media Institute of Scientific and Industrial ResearchOsaka UniversityOsaka 565-0871Japan

Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the *** benchmark quantitatively and qualitatively.

关键词： image matting global context deformable sampling

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Solving the Convex Flow Problem 63

Solving the Convex Flow Problem

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Diamandis, Theo Angeris, Guillermo Massachusetts Insttitue of Technology Computer Science and Artificial Intelligence Lab CambrigdeMA United States Bain Capital BostonMA United States

ISBN: (纸本)9798350316339

In this paper, we introduce the solver ConvexFlows for the convex flow problem first defined in the authors' previous work. In this problem, we aim to optimize a concave utility function depending on the flows over a graph. However, unlike the classic network flows literature, we also allow for a concave relationship between the input and output flows of edges. This nonlinear gain describes many physical phenomena, including losses in power network transmission lines. We outline an efficient algorithm for solving this problem which parallelizes over the graph edges. We provide an open source implementation of this algorithm in the Julia programming language package ***. This package includes an interface to easily specify these flow problems. We conclude by walking through an example of solving the optimal power flow using ConvexFlows. © 2024 IEEE.

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Full-duplex strategy for video object segmentation

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Computational Visual Media 2023年第1期9卷 155-175页

作者： Ge-Peng Ji Deng-Ping Fan Keren Fu Zhe Wu Jianbing Shen Ling Shao School of Computer Science Wuhan UniversityWuhanChina Computer Vision Lab ETH ZurichETF C113.2Sternwartstrasse 78092 ZurichSwitzerland College of Computer Science Sichuan UniversityChengduChina Peng Cheng Laboratory ShenzhenChina School of Computer Science Beijing Institute of TechnologyBeijingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two *** this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding ***,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding *** improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the *** experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object *** project is publicly available at https://***/GewelsJI/FSNet.

关键词： video object segmentation(VOS) video salient object detection(V-SOD) visual attention

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Performing Aerobatic Maneuver with Imitation Learning 23rd

Performing Aerobatic Maneuver with Imitation Learning

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23rd International Conference on Computational science, ICCS 2023

作者： Freitas, Henrique Camacho, Rui Silva, Daniel Castro Department of Informatics Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias s/n. Porto4200-465 Portugal LIAAD/INESC TEC - Artificial Intelligence and Decision Support Laboratory/Institute for Systems and Computer Engineering Technology and Science Porto Portugal LIACC - Artificial Intelligence and Computer Science Laboratory Porto Portugal

ISBN: (纸本)9783031359941

The work reported in this article addresses the challenge of building models for non-trivial aerobatic aircraft maneuvers in an automated fashion. It is built using a Behavioural Cloning approach where human pilots provide a set of example maneuvers used by a Machine Learning algorithm to induce a control model for each maneuver. The best examples for each maneuver were selected using a set of objective evaluation metrics. Using those example sets, robust models were induced that could replicate (and in some cases outperform) the human pilots that provided the examples (the clean-up effect). Complete complex maneuvers were performed using a meta-controller capable of sequencing the basic ones learned by imitation. This endeavor was rewarded by the results that show several Machine Learning models capable of performing highly complex aircraft maneuvers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloning

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StaR Maps: Unveiling Uncertainty in Geospatial Relations 27

StaR Maps: Unveiling Uncertainty in Geospatial Relations

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Flade, Benedict Kohaut, Simon Eggert, Julian Dhami, Devendra Singh Kersting, Kristian Honda Research Institute Europe GmbH Offenbach63073 Germany Artificial Intelligence and Machine Learning Lab Department of Computer Science Tu Darmstadt Darmstadt64283 Germany Tu Eindhoven Uncertainty in Artificial Intelligence Group Department of Mathematics and Computer Science MB Eindhoven5600 Netherlands Hessian Ai Centre for Cognitive Science Germany

ISBN: (纸本)9798331505929

The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncertainties, which are commonly underrepresented by the respective geographic information systems. Hence, it is critical to develop a representation that combines a statistical, probabilistic perspective with the relational nature of geospatial data. Further, such a representation should facilitate an honest view of the data's accuracy and provide an environment for high-level reasoning to obtain novel insights from task-dependent queries. Our work addresses this gap in two ways. First, we present Statistical Relational Maps (StaR Maps) as a representation of uncertain, semantic map data. Second, we demonstrate efficient computation of StaR Maps to scale the approach to wide urban spaces. Through experiments on real-world, crowd-sourced data, we underpin the application and utility of StaR Maps in terms of representing uncertain knowledge and reasoning for complex geospatial information. © 2024 IEEE.

关键词： Knowledge representation

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Mapping Rats Movements: Tracking and Visualizing Heatmaps and Trajectories Using computer Vision 2

Mapping Rats Movements: Tracking and Visualizing Heatmaps an...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Mostafa, Khaled Hany, Mohamed Ashraf, Abdelaziz Zaafan, Mai Atia, Ayman Faculty of Pharmacy Giza Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Giza Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350362633

Rats play a crucial role in pharmacological research, serving as valuable models for studying the effects of drugs and treatments on various physiological and behavioural parameters. The proposed system effectively tracks, detects, and maintains rats' identities while also extracting heatmaps and full movement trajectories for extensive analysis. Our approach leverages the YOLOv5 model for accurate rat detection and DeepSORT for reliable tracking. Each rat is assigned a unique ID, and the system includes a proposed tracking region to maintain identification despite occlusions or undetected frames. The tracking experiment achieved a high MOTA score of 99.24%, demonstrating the system's robustness and accuracy in diverse experimental conditions. Our methodology proves effective in maintaining precise identification even in challenging scenarios, highlighting its potential for enhancing pharmacological research. © 2024 IEEE.

关键词： Trajectories

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