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arXiv 2025年

作者： Aoki, Toshiaki Tomita, Takashi Kawai, Tatsuji Kawakami, Daisuke Chida, Nobuo Graduate School of Advanced Science and Technology JAIST Japan Research Center for Advanced Computing Infrastructure JAIST Japan Department of Computer Science Kochi University Japan Mitsubishi Electric Corporation Japan

Autonomous driving systems are typically verified based on scenarios. To represent the positions and movements of cars in these scenarios, diagrams that utilize icons are typically employed. However, the interpretation of such diagrams is typically ambiguous, which can lead to misunderstandings among users, making them unsuitable for the development of high-reliability systems. To address this issue, this study introduces a notation called the car position diagram (CPD). The CPD allows for the concise representation of numerous scenarios and is particularly suitable for scenario analysis and design. In addition, we propose a method for converting CPD-based models into propositional logic formulas and enumerating all scenarios using a SAT solver. A tool for scenario enumeration is implemented, and experiments are conducted on both typical car behaviors and international standards. The results demonstrate that the CPD enables the concise description of numerous scenarios, thereby confirming the effectiveness of our scenario analysis method. Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Monitoring Social Distancing in Smart Spaces using Infrastructure-Based Sensors 7

Monitoring Social Distancing in Smart Spaces using Infrastru...

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： Motlagh, Naser Hossein Toivonen, Pupu Zaidan, Martha Arbayani Lagerspetz, Eemil Peltonen, Ella Gilman, Ekaterina Nurmi, Petteri Tarkoma, Sasu University of Helsinki Department of Computer Science Finland Finland University of Oulu Center for Ubiquitous Computing Oulu Finland

ISBN: (纸本)9781665444316

Social distancing is a critical tool for mitigating disease transmission, particularly in crowded indoor spaces. In this paper, we contribute by assessing the feasibility of re-purposing existing infrastructure of occupancy monitoring sensors and environmental sensors for the dual purpose of monitoring social distancing and supporting disease transmission risk estimation. We consider 410 continuous days of measurements from CO2 and PIR (passive infrared) motion detectors collected from a collaborative smart space, prior to the start of the pandemic in 2017-2018. We demonstrate how these sensors can be used to estimate occupancy levels, as well as analyze occupancy patterns within the space. We also consider the use of overall air quality within the space for estimating insights about potential transmission risks. Based on our analysis, we derive insights into how infrastructure-based sensors can be used to detect problematic areas in the space and offer guidelines on how to modify these areas to be more social distancing aware. © 2021 IEEE.

关键词： Internet of things

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Causal-IQA: towards the generalization of image quality assessment based on causal inference 24

Causal-IQA: towards the generalization of image quality asse...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yan Zhong Xingyu Wu Li Zhang Chenxi Yang Tingting Jiang School of Mathematical Sciences and National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China Hefei China National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science and 5National Biomedical Imaging Center Peking University Beijing China

Due to the high cost of Image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA methods. To address this, this paper proposes a novel end-to-end blind IQA method: Causal-IQA. Specifically, we first analyze the causal mechanisms in IQA tasks and construct a causal graph to understand the interplay and confounding effects between distortion types, image contents, and subjective human ratings. Then, through shifting the focus from correlations to causality, Causal-IQA aims to improve the estimation accuracy of image quality scores by mitigating the confounding effects using a causality-based optimization strategy. This optimization strategy is implemented on the sample subsets constructed by a Counterfactual Division process based on the Backdoor Criterion. Extensive experiments illustrate the superiority of Causal-IQA.

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Efficient learning of continuous-variable quantum states

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Physical Review Research 2024年第3期6卷 033280页

作者： Ya-Dong Wu Yan Zhu Giulio Chiribella Nana Liu John Hopcroft Center for Computer Science QICI Quantum Information and Computation Initiative Department of Computer Science The Department of Computer Science for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Institute of Natural Sciences Ministry of Education Key Laboratory in Scientific and Engineering Computing School of Mathematical Sciences University of Michigan-

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation, and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to reconstruct the full quantum state, but rather to estimate its characteristic function at a given set of points. For multimode states with reflection symmetry, we show that the characteristic function at M points can be estimated using only O(logM) copies of the state, independently of the number of modes. When the characteristic function is known to be positive, as in the case of squeezed vacuum states, the estimation is achieved by an experimentally friendly setup using only beamsplitters and homodyne measurements.

关键词： Machine learning Quantum information processing with continuous variables Quantum tomography

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Analysis of Word-level Embeddings for Indic Languages on AI4Bharat-IndicNLP Corpora 8

Analysis of Word-level Embeddings for Indic Languages on AI4...

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8th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer Engineering, UPCON 2021

作者： Goswami, Dipam Malviya, Shrikant Mishra, Rohit Tiwary, Uma Shanker Bits Pilani Department of Computer Science and Information Systems India Iiit Allahabad Center for Cognitive Computing India

ISBN: (纸本)9781665409629

This paper presents the analysis of non-contextual word embeddings trained on AI4Bharat-IndicNLP corpus containing 2.7 billion words covering 10 Indian languages. We share the pre-trained embeddings for research and development in Indic languages. These embeddings are evaluated on several evaluation tasks like word similarity and analogy evaluation, classification tasks on multiple datasets. The analysis of word embeddings is expected to give researchers a better understanding of the Indic Languages. We show that Word2Vec skip-gram and FastText skip-gram embeddings are the best performing models for NLP tasks on Indic languages. All the embeddings are made freely available. © 2021 IEEE.

关键词： Sentiment analysis

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Comparing the Effect of Code Optimizations on Simulation Runtime Across Synchronous Cellular Automata Models of HIV

Comparing the Effect of Code Optimizations on Simulation Run...

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2021 Winter Simulation Conference, WSC 2021

作者： Li, Junjiang Giabbanelli, Philippe J. Koster, Till Miami University Department of Computer Science Software Engineering OH45056 United States Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

Models developed by domain experts occasionally struggle to achieve a sufficient execution speed. Improving performances requires expertise in parallel and distributed simulations, hardware, or time to profile performances to identify bottlenecks. However, end-users in biological simulations of the Human Immunodeficiency Virus (HIV) have repeatedly demonstrated that these resources are either not available or not sought, resulting in models that are developed through user-friendly languages and platforms, then used on workstations. This situation becomes problematic when performances cannot cope with the salient characteristics of the phenomenon that is modeled, as is the case with cellular automata (CA) models of HIV. In this paper, we optimize the Python code of CA models of HIV to scale the number of cells handled by a simulation on a workstation commonly available to end-users. We demonstrate this scalability via five HIV CA models and compare these results to assess how modeling choices can impact runtime. © 2021 IEEE.

关键词： Viruses

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Deep Learning Based End-to-End Specular Reflection Removal for Medical Endoscopic Images 23

Deep Learning Based End-to-End Specular Reflection Removal f...

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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Shih, Chi-Sheng Liao, Yu-Cheng Tan, Ching-Ting Graduate Institute of Networking and Multimedia Department of Computer Science and Information Engineering High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Otolaryngology National Taiwan University Hospital Taipei Taiwan

ISBN: (纸本)9798400702280

Endoscopic images usually have many overexposed regions due to strong and focused light sources and, consequently, physicians need to change the camera angle for a clear view from time to time. This work targets removing the specular reflections from the endoscopy images but maintaining the structural and semantic texture in the images. Unlike the state-of-the-art image inpainting methods, the proposed method treats the specular reflection removal problem as an image-to-image translation task by skipping the specular detection step. The technique uses Dense Multi-scale Fusion Network (DMFN), which aims for small size and clear images, as the base and revises the network to support the large size and noisy endoscopy images. Moreover, the method can recover structure from large overexposed regions. Experimental results show that under different glare ratios and granularity settings, it is more effective than the Partial Convolution Model when recovering the corrupted pixels: the mean average error of pixel values decreases 13.2%, and the PSNR value increases 2.62dB. Qualitative analysis shows that even in the worst case when the proposed model generates output images with MAE up to 4.30, its recoveries still possess good visual qualities. The proposed model can process a 480 × 768 image in 16.2ms on a computer equipped with 36 CPU cores and an RTX-3090 GPU. It usually takes at least 20 minutes for a human to recover such images. © 2023 ACM.

关键词： Endoscopy

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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

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IEEE Transactions on Services computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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Towards Scalable Quantum Key Distribution: A Machine Learning-Based Cascade Protocol Approach

arXiv

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arXiv 2024年

作者： Al-Mohammed, Hasan Abbas Al-Kuwari, Saif Kuniyil, Hashir Farouk, Ahmed The Department of Computer Science and Engineering Qatar University Doha Qatar The Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar The Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt

Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol’s integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication. © 2024, CC BY.

关键词： Quantum communication

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Unsupervised Cortical Surface Registration Network for Aligning Gyralnet 22

Unsupervised Cortical Surface Registration Network for Align...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Cheng, Jiale Zhao, Fenqiang Hu, Dan Cao, Chao Wu, Zhengwang Yuan, Xinrui Han, Kangfu Zhang, Lu Liu, Tianming Zhu, Dajiang Li, Gang University of North Carolina Department of Radiology Biomedical Research Imaging Center Chapel Hill NC United States University of North Carolina at Chapel Hill North Carolina State University Joint Department of Biomedical Engineering NC United States University of Texas Department of Computer Science and Engineering Arlington TX United States Indiana University Department of Computer Science Indianapolis IN United States School of Computing University of Georgia GA United States

ISBN: (纸本)9798331520526

The cortical 3-hinge gyrus (3HG) and its network (GyralNet) play key roles in understanding the regularity and variability of brain structure and function. However, existing cortical surface registration methods overlook these features, resulting in suboptimal alignment across subjects. Currently, no 3HG and GyralNet atlas exist for registration, and generation of the corresponding atlas requires extensive runtime using traditional methods. To enable better registration of these features, we introduce an unsupervised learning framework to jointly develop 3HGs and GyralNet atlas and register the individual cortical features onto the atlas. To incorporate the graph structure of 3HGs and GyralNet into the registration network, we convert them into surface distance maps, facilitating effective integration. To effectively learn large deformations, a multi-level spherical registration network based on spherical U-Net is introduced to perform registration in a coarse-to-fine manner. Experiments demonstrate our approach's ability to generate 3HGs and GyralNet atlas with detailed patterns and effectively improve registration accuracy. © 2025 IEEE.

关键词： 3-Hinge Gyrus Cortical Surface Registration GyralNet Spherical U-net

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