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Electronics Design Automation (ISEDA), International Symposium of

作者： Ye Cai Yuyao Liang Zhipeng Luo Biwei Xie Xingquan Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China Center of Machine Learning and Optimization Minnan Normal University Zhangzhou China State Key Lab of Processors Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

Accurate parasitic capacitance extraction becomes increasingly essential in advanced technology nodes. 2.5D ex-traction requires more effort to maintain accuracy comparable to the 3D solver. In this work, two experiments reveal that error deviation in the 2.5D method enlarges as wire width shrinks and length increases. A novel Gauss law-based point cloud is proposed to model the 3D capacitance problem. With the proposed data representation, a transformer-based neural network architecture, called PCT-Cap, is designed for 3D pattern matching. PCT-Cap exhibits much better performance than the ResNet-based capacitance models. Extensive experiments on 28nm technology demonstrate that PCT-Cap can accurately predict over 95.41 % of total capacitance with an error margin of less than 5 %. Additionally, PCT-Cap achieves 95.90 % accuracy in predicting coupling capacitance within a margin of error of less than 10%. PCT-Cap achieves at least a 55 × speeding up compared with the commercial extraction tool while consuming negligible memory.

关键词： Point cloud compression Solid modeling Three-dimensional displays Accuracy Capacitance Feature extraction Transformers

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Domain-adaptive Graph based on Post-hoc Explanation for Cross-domain Hate Speech Detection

Domain-adaptive Graph based on Post-hoc Explanation for Cros...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yushan Jiang Bin Zhou Xuechen Zhao Jiaying Zou Feng Xie Liang Li School of Computer National University of Defence Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defence Technology Changsha China

Hate speech detection is hampered by the scarcity and topical and lexical biases of annotated data, leading to poor generalization. It is imperative to devise a cross-domain approach to solve this problem. The ability to learn transferable knowledge is critical for cross-domain hate speech detection. In this work, We propose a domain-adaptive dependency graph method based on post-hoc explanation (DPDG). We extract post-hoc explanations from fine-tuned BERT classifiers as the importance score for hate representation. Based on these, we construct in-domain graph and cross-domain graph to better learn in-domain hate representation and adapt to the target domain respectively. Finally, we use interactive GCN blocks to interactively and adaptively learn and adjust the domain adaptive graph representation. The results of cross-domain experiments on multiple domains show that our proposed model outperforms competitive baselines in cross-domain hate speech detection.

关键词： Weight measurement Adaptation models Analytical models Image edge detection Hate speech Neural networks Natural languages

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Doping Shortens the Metal/Metal Distance and Promotes OH Coverage in Non-Noble Acidic Oxygen Evolution Reaction Catalysts

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Journal of the American Chemical Society 2023年第14期145卷 7829-7836页

作者： Wang, Ning Ou, Pengfei Miao, Rui Kai Chang, Yuxin Wang, Ziyun Hung, Sung-Fu Abed, Jehad Ozden, Adnan Chen, Hsuan-Yu Wu, Heng-Liang Huang, Jianan Erick Zhou, Daojin Ni, Weiyan Fan, Lizhou Yan, Yu Peng, Tao Sinton, David Liu, Yongchang Liang, Hongyan Sargent, Edward H. Department of Electrical and Computer Engineering University of Toronto 35 St George Street TorontoONM5S 1A4 Canada School of Materials Science and Engineering Key Laboratory of Efficient Utilization of Low and Medium Grade Energy Ministry of Education Tianjin University Tianjin300350 China Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada School of Chemical Sciences The University of Auckl Auckland1010 New Zealand Department of Applied Chemistry National Yang Ming Chiao Tung University Hsinchu300 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei10617 Taiwan Center of Atomic Initiative for New Materials National Taiwan University Taipei10617 Taiwan State Key Lab of Hydraulic Engineering Simulation and Safety School of Materials Science & Engineering Tianjin University Tianjin300354 China

Acidic water electrolysis enables the production of hydrogen for use as a chemical and as a fuel. The acidic environment hinders water electrolysis on non-noble catalysts, a result of the sluggish kinetics associated with the adsorbate evolution mechanism, reliant as it is on four concerted proton-electron transfer steps. Enabling a faster mechanism with non-noble catalysts will help to further advance acidic water electrolysis. Here, we report evidence that doping Ba cations into a Co3O4 framework to form Co3-xBaxO4 promotes the oxide path mechanism and simultaneously improves activity in acidic electrolytes. Co3-xBaxO4 catalysts reported herein exhibit an overpotential of 278 mV at 10 mA/cm2 in 0.5 M H2SO4 electrolyte and are stable over 110 h of continuous water oxidation operation. We find that the incorporation of Ba cations shortens the Co-Co distance and promotes OH adsorption, findings we link to improved water oxidation in acidic electrolyte. © 2023 American Chemical Society.

关键词： Catalysts

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Security Considerations for Virtual Reality Systems

arXiv

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arXiv 2022年

作者： Viswanathan, Karthik Yazdinejad, Abbas Department of Software Engineering Game Development Kennesaw State University GA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada

There is a growing need for authentication methodology in virtual reality applications. Current systems assume that the immersive experience technology is a collection of peripheral devices connected to a personal computer or mobile device. Hence there is a complete reliance on the computing device with traditional authentication mechanisms to handle the authentication and authorization decisions. Using the virtual reality controllers and headset poses a different set of challenges as it is subject to unauthorized observation, unannounced to the user given the fact that the headset completely covers the field of vision in order to provide an immersive experience. As the need for virtual reality experiences in the commercial world increases, there is a need to provide other alternative mechanisms for secure authentication. In this paper, we analyze a few proposed authentication systems and reached a conclusion that a multidimensional approach to authentication is needed to address the granular nature of authentication and authorization needs of a commercial virtual reality applications in the commercial world. © 2022, CC BY.

关键词： Virtual reality

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Detection of hidden data attacks combined fog computing and trust evaluation method in sensor-cloud system

Detection of hidden data attacks combined fog computing and ...

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作者： Zhang, Guangxue Wang, Tian Wang, Guojun Liu, Anfeng Jia, Weijia College of Computer Science and Technology Huaqiao University Xiamen China School of Computer Science and Educational Software Guangzhou University Guangzhou China School of Information Science and Engineering Central South University Hunan China State Key Laboratory of Internet of Things for Smart City University of Macau China

With the popularity of Sensor-Cloud, its security issues get more attention from industry and academia. Especially, Sensor-Cloud underlying network is very vulnerable to internal attacks due to its limitations in computing, storage, and analysis. Most existing trust evaluation mechanisms are proposed to detect internal attack issues from the behavior level. However, there are some special internal attacks in the data level such as hidden data attacks, which are normal in the behavior level but generate malicious data to lead user to make wrong decisions. To detect this type of attacks, we design a fog-based detection system (FDS), which is based on the trust evaluation mechanism in the behavior level. In this paper, three types of scenes (the redundant data, the parameter curve characteristic, and the data validation) are defined, and three detection schemes are given. Some experiments are conducted, which manifest that FDS has certain advantages in detecting hidden data attacks. © 2018 John Wiley & Sons, Ltd.

关键词： Fog computing

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Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement

Multitask Learning Neural Networks for Pandemic Prediction w...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Zhong Zhang Feng Xie Xuechen Zhao Bin Zhou Jian Chen Lei Tian School of Computer National University of Defence Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defence Technology Changsha China

state and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules: (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. The proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks.

关键词： COVID-19 Correlation Pandemics Social networking (online) Fuses Convolution Multitasking

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Challenges of Implementing Agile Processes in Remote-First Companies

arXiv

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arXiv 2022年

作者： Asfaw, Lulit Clemmons, Mikael Hayes, Cody Letnaunchyn, Elise Rabieinejad, Elnaz College of Computing and Software Engineering Kennesaw State University MariettaGA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada

The trend of remote work, especially in the IT sector, has been on the rise in recent years, and its popularity has especially increased since the COVID-19 pandemic. In addition to adopting remote work, companies also have been migrating toward managing their projects using agile processes. Agile processes promote small and continuous feedback loops powered by effective communication. In this survey, we look to discover the challenges of implementing these processes in a remote setting, specifically focusing on the impact on communication. We examine the role communication plays in an agile setting and look for ways to mitigate the risk remote environments impose on it. Lastly, we present other miscellaneous challenges companies could experience that still carry dangers but are less impactful overall to agile implementation. © 2022, CC BY.

关键词： COVID-19

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Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

Human Observation-Inspired Trajectory Prediction for Autonom...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Haicheng Liao Shangqian Liu Yongkang Li Zhenning Li Chengyue Wang Yunjian Li Shengbo Eben Li Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of Macau Macau SAR China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Departments of Civil and Environmental Engineering and Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of Macau Macau SAR China Faculty of Innovation Engineering Macau University of Science and Technology Macau SAR China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel “adaptive visual sector” mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a “dynamic traffic graph” using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs.

关键词： Adaptation models Visualization Time series analysis Predictive models Cognition Trajectory Convolutional neural networks

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CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2022年

作者： Lin, Yuqi Chen, Minghao Wang, Wenxiao Wu, Boxi Li, Ke Lin, Binbin Liu, Haifeng He, Xiaofei State Key Lab of CAD&CG College of Computer Science Zhejiang University China School of Software Technology Zhejiang University China Fullong Technology China

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) focus on confident regions. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://***/linyq2117/CLIP-ES. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Analyzing XAI Metrics: Summary of the Literature Review

TechRxiv

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TechRxiv 2022年

作者： Sisk, Marion Majlis, Makeen Page, Cameron Yazdinejad, Abbas College of Computing and Software Engineering Kennesaw State University MariettaGA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada

The widespread adoption of Artificial Intelligence (AI) models by various industries in recent years have made Explainable Artificial Intelligence (XAI) an active field of research. This adoption can cause trust and effectiveness to suffer if the results of these models are not favorable in some way. XAI has advanced to the point where many metrics have been proposed as reasons for the outputs of many AI models. However, there is little consensus about what technical metrics are most important, nor is there a consensus on how best to analyze explainable methods and models. A discussion of varying attempts at this is brought forth, but the paper also goes into the ethics of AI and its societal impact. Given the modern ubiquity with which AI exists and the immensely multidisciplinary approach, AI has evolved into, using only technical metrics cannot fully describe XAI’s effectiveness. This paper explores several approaches to measuring the ethical effects of XAI, whether it has any bearing on modern research, as well as how the impacts of AI and XAI are measured on society. The full attempt at quantifying XAI models’ effectiveness is explored from a technical and non-technical point of view. © 2022, CC BY.

关键词： Artificial intelligence

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