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arXiv 2021年

作者： Chen, Haipeng Qiu, Wei Ou, Han-Ching An, Bo Tambe, Milind Center for Research on Computation and Society Harvard University School of Computer Science and Engineering Nanyang Technological University

The influence maximization (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of IM problems, where whether the nodes are willing to be the seeds when being invited is uncertain, called contingency-aware IM. Such contingency aware IM is critical for applications for non-profit organizations in low resource communities (e.g., spreading awareness of disease prevention). Despite the initial success, a major practical obstacle in promoting the solutions to more communities is the tremendous runtime of the greedy algorithms and the lack of high performance computing (HPC) for the non-profits in the field – whenever there is a new social network, the non-profits usually do not have the HPCs to recalculate the solutions. Motivated by this and inspired by the line of works that use reinforcement learning (RL) to address combinatorial optimization on graphs, we formalize the problem as a Markov Decision Process (MDP), and use RL to learn an IM policy over historically seen networks, and generalize to unseen networks with negligible runtime at test phase. To fully exploit the properties of our targeted problem, we propose two technical innovations that improve the existing methods, including state-abstraction and theoretically grounded reward shaping. Empirical results show that our method achieves influence as high as the state-of-the-art methods for contingency-aware IM, while having negligible runtime at test phase. © 2021, CC BY-NC-ND.

关键词： Combinatorial optimization

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Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand

arXiv

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arXiv 2023年

作者： Guo, Junfeng Li, Yiming Wang, Lixu Xia, Shu-Tao Huang, Heng Liu, Cong Li, Bo Department of Computer Science University of Maryland United States ZJU-Hangzhou Global Scientific and Technological Innovation Center China School of Cyber Science and Technology Zhejiang University China Department of Computer Science Northwestern University United States Tsinghua Shenzhen International Graduate School Tsinghua University China Department of Electronic and Computer Engineering UC Riverside United States Department of Computer Science University of Illinois Urbana-Champaign United States Department of Computer Science University of Chicago United States

The prosperity of deep neural networks (DNNs) is largely benefited from open-source datasets, based on which users can evaluate and improve their methods. In this paper, we revisit backdoor-based dataset ownership verification (DOV), which is currently the only feasible approach to protect the copyright of open-source datasets. We reveal that these methods are fundamentally harmful given that they could introduce malicious misclassification behaviors to watermarked DNNs by the adversaries. In this paper, we design DOV from another perspective by making watermarked models (trained on the protected dataset) correctly classify some ‘hard’ samples that will be misclassified by the benign model. Our method is inspired by the generalization property of DNNs, where we find a hardly-generalized domain for the original dataset (as its domain watermark). It can be easily learned with the protected dataset containing modified samples. Specifically, we formulate the domain generation as a bi-level optimization and propose to optimize a set of visually-indistinguishable clean-label modified data with similar effects to domain-watermarked samples from the hardly-generalized domain to ensure watermark stealthiness. We also design a hypothesis-test-guided ownership verification via our domain watermark and provide the theoretical analyses of our method. Extensive experiments on three benchmark datasets are conducted, which verify the effectiveness of our method and its resistance to potential adaptive methods. The code for reproducing main experiments is available at https://***/JunfengGo/Domain-Watermark. © 2023, CC BY.

关键词： Deep neural networks

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Identification Over Quantum Broadcast Channels

arXiv

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arXiv 2022年

作者： Rosenberger, Johannes Deppe, Christian Pereg, Uzi Technical University of Munich Germany School of Computation Information and Technology Department of Computer Engineering Germany Munich Center for Quantum Science and Technology Munich Germany Technion – Israel Institute of Technology Haifa Israel Helen Diller Quantum Center Department of Electrical and Computer Engineering

Identification over quantum broadcast channels is considered. As opposed to the information transmission task, the decoder only identifies whether a message of his choosing was sent or not. This relaxation allows for a double-exponential code size. An achievable identification region is derived for a quantum broadcast channel, and a full characterization for the class of classical-quantum broadcast channels. The identification capacity region of the single-mode pure-loss bosonic broadcast channel is obtained as a consequence. Furthermore, the results are demonstrated for the quantum erasure broadcast channel, where our region is suboptimal, but improves on the best previously known *** Codes 81P45 (Primary) 94A15, 94A40, 94A24 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Bosons

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study 6

High-Resolution Optical Imaging Incorporated Non-Destructive...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Hakmana, Nethmi Tharushi Kahatapitiya, Nipun Shantha Weerasinghe, Sasmitha Kalupahana, Deshan Silva, Bhagya Nathali Ravichandran, Naresh Kumar Seong, Daewoon Han, Sangyeob Lee, Seung Yeol June, Hee-Young Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga Faculty of Engineering Sri Lanka Institute of Information Technology Department of Mechanical Engineering Malabe Sri Lanka Faculty of Engineering University of Sri Jayewardenepura Department of Computer Engineering Nugegoda Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Korea Republic of School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Korea Republic of ICT Convergence Research Center Kyungpook National University Daegu Korea Republic of Kyungpook National University Department of Plant Medicine Daegu Korea Republic of Institute of Plant Medicine Kyungpook National University Daegu Korea Republic of Faculty of Engineering Sri Lanka Institute of Information Technology Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management. © 2024 IEEE.

关键词： Fruits

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Analysis of a Data Set to Determine the Dependence of Airline Passenger Satisfaction 7th

Analysis of a Data Set to Determine the Dependence of Airlin...

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7th computational Methods in Systems and Software, CoMeSySo 2023

作者： Tynchenko, V.S. Borodulin Kleshko, I.I. Nelyub, V.A. Rukosueva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia

ISBN: (纸本)9783031535512

This paper presents an analysis of a data set to determine the factors influencing airline passenger satisfaction. The study examines various criteria such as gender, customer type, age, travel type, class, and range to assess their impact on passenger satisfaction. The dataset consists of 25 columns, including attributes like Wi-Fi availability, convenience of online booking, seat comfort, in-flight entertainment, baggage handling, and overall satisfaction. The sample is relatively balanced, with equal representation of men and women, predominantly repeat customers, and a majority flying for business purposes. Key findings include a strong correlation between departure and arrival delays, higher satisfaction among passengers in business class, and positive ratings for Wi-Fi service correlating with overall satisfaction. Correlation analysis reveals interdependencies between different attributes, such as the influence of cleanliness on seat comfort and food and beverage ratings. In addition, a neural network forecasting model is used to estimate the average ratings of passengers, although with low accuracy, which was later excluded. Finally, a decision tree algorithm is utilized to identify the most significant attributes affecting passenger satisfaction words. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Baggage handling

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Correlation Analysis and Predictive Factors for Building a Mathematical Model 1

引用

7th computational Methods in Systems and Software, CoMeSySo 2023

作者： Nelyub, V.A. Tynchenko, V.S. Gantimurov, A.P. Degtyareva, K.V. Kukartseva, O.I. Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

The study, published in the journal Nature Medicine, looked at data on 1,000 people from China who were tracked over an average period of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution. The study analyzed data on patients with lung cancer, including their age, gender, exposure to air pollution, alcohol consumption, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoking, chest pain, cough, hemoptysis, fatigue, weight loss, shortness of breath, wheezing, difficulty swallowing, nail thickening and snoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Air pollution

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Ultra Low Power High Speed DFT Implementation For ASIC SoC

Ultra Low Power High Speed DFT Implementation For ASIC SoC

引用

International Conference for Convergence of Technology (I2CT)

作者： K. Madhava Rao Pattem Sampath Kumar T. Vasudeva Reddy D Nilima K Saikumar Abbas Mahdi Khlaif Dept of ECE B V Raju Institute of Technology Narsapur Medak Telangana Department of ECE Malla Reddy College of Engineering Maisammaguda Dhulapally Secunderabad Department of Computer Science and Information Technology MLR Institute of Technology Hyderabad Telangana India Computer Science School of Engineering Malla Reddy University Maisammaguda Dulapally Hyderabad Telangana Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

An ultra-low power DFT architecture suitable for MIMO-OFDM applications & it was introduced in this study. Several radix such as 8/16/32 dependent optimization methods are performed to create variable lengths of 4k/8k points, a necessity in today's 4G and 5G communication technologies, which are plagued by a number of power and performance concerns with DFT processors. These technologies are widely used but have a higher energy footprint than others. Consequently, this work uses a novel approach called "SBOX" and the concept of parallelism to boost performance and reduce power consumption, all of which are essential for an advanced DFT architecture. Parallelism and the proposed SBOX model allow for fewer multiplications and faster processing times. Cadence-allegro-17.2 is used for the design and verification of the full implementation; this software is also used for simulation, synthesis, layout, and power analysis. The area required for 8-point s-box DFT is 105.15 um2, it requires 7mW of power, and it can achieve a throughput of 59.33GBPS. In addition, the throughput of 57.33GBPS may be achieved by 16-point s-box DFT with a footprint of 123.35um2, using 9mW of power. Also, the throughput of 32-point s-box DFT is 56.45GBPS on a chip that uses 10.24 mW and occupies 132.35um2. With MIMO in 5G technology, this layout becomes more practical for usage in telecommunications. The performance is enhanced over existing architectures thanks to the optimized radix 8 s-box DFT.

关键词： 5G mobile communication Discrete Fourier transforms Layout computer architecture Parallel processing Throughput Software

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Neural Network Student Cognitive Diagnosis Model Based on Multi-Relational Graph 25

Neural Network Student Cognitive Diagnosis Model Based on Mu...

引用

Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and Information Security

作者： Ze Han Yu Su Qi Mo Xinyu Nie School of Electronic and Information Engineering Anhui University Hefei Anhui China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei Anhui China School of Computer Science and Artificial Intelligence Hefei Normal University Hefei Anhui China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei Anhui China AHU-IAI AI Joint Laboratory Anhui University Hefei Anhui China School of Electronic and Information Engineering Anhui University Hefei Anhui China

ISBN: (纸本)9798400713453

The Internet has enabled online learning to become an important means for students to acquire knowledge, and student cognitive diagnosis models have played a key role in this process. These models can evaluate students' learning behaviors and knowledge mastery, providing support for personalized learning. Unfortunately, traditional models have their limitations when dealing with complex relational data related mainly to the relationship between students and problems, while the characteristics as well as potential relationships among students are almost left neglected. In response, this paper proposes a Neural Network Student Cognitive Diagnosis Model Based on Multi-Relational Graph (MGNCD) that brings together the powers of Graph Attention Network (GAT) and Relational Graph Convolutional Network (RGCN) to model multi-dimensional heterogeneous nodes such as students, classes, problems, and skills. The introduction of various edge types and random edge connection strategies allowed the model to tackle data sparsity and heterogeneity. Experimental results indicate that the proposed model outperforms traditional methods in accuracy (ACC) and area under the curve (AUC), which provides an encouraging novel insight into cognitive diagnosis.

关键词： Cognitive diagnosis

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A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing

arXiv

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arXiv 2024年

作者： Feng, Yujie Yang, Yin Fan, Xiaohong Zhang, Zhengpeng Bu, Lijing Zhang, Jianping School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory for Intelligent Computing and Information Processing of the Ministry of Education Xiangtan411105 China School of Mathematics and Computational Science Xiangtan University National Center for Applied Mathematics in Hunan Hunan International Scientific and Technological Innovation Cooperation Base of Computational Science Xiangtan411105 China College of Mathematical Medicine Zhejiang Normal University Jinhua321004 China School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

Recently, deep learning methods have gained remarkable achievements in the field of image restoration for remote sensing (RS). However, most existing RS image restoration methods focus mainly on conventional first-order degradation models, which may not effectively capture the imaging mechanisms of remote sensing images. Furthermore, many RS image restoration approaches that use deep learning are often criticized for their lacks of architecture transparency and model interpretability. To address these problems, we propose a novel progressive restoration network for high-order degradation imaging (HDI-PRNet), to progressively restore different image degradation. HDI-PRNet is developed based on the theoretical framework of degradation imaging, offering the benefit of mathematical interpretability within the unfolding network. The framework is composed of three main components: a module for image denoising that relies on proximal mapping prior learning, a module for image deblurring that integrates Neumann series expansion with dual-domain degradation learning, and a module for super-resolution. Extensive experiments demonstrate that our method achieves superior performance on both synthetic and real remote sensing images. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Multidimensional biomarkers for multiple system atrophy:an update and future directions

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Translational Neurodegeneration 2023年第1期12卷 357-373页

作者： Linlin Wan Sudan Zhu Zhao Chen Rong Qiu Beisha Tang Hong Jiang Department of Neurology Xiangya HospitalCentral South UniversityChangsha 410008China Department of Neurology The Third Xiangya HospitalCentral South UniversityChangsha 410013China Key Laboratory of Hunan Province in Neurodegenerative Disorders Central South UniversityChangsha 410008China National Clinical Research Center for Geriatric Disorders Xiangya HospitalCentral South UniversityChangsha 410008China Department of Radiology Xiangya HospitalCentral South UniversityChangsha 410008China Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases Changsha 410008China School of Computer Science and Engineering Central South UniversityChangsha 410083China National International Collaborative Research Center for Medical Metabolomics Central South UniversityChangsha 410008China

Multiple system atrophy(MSA)is a fatal progressive neurodegenerative *** are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying *** recent years,significant research efforts have been made in exploring multidimensional biomarkers for ***,currently few biomarkers are available in *** this review,we systematically summarize the latest advances in multidimensional biomarkers for MSA,including biomarkers in fluids,tissues and gut microbiota as well as imaging *** directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.

关键词： Multiple system atrophy Biomarker Fluid Tissue Gut microbiota Imaging

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