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2024 International Conference on Artificial Intelligence and Machine Learning Research, CAIMLR 2024

作者： Li, Yize Gao, Zhijun Liu, Yunpeng School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Opto-Electronic Information Technology Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781510691797

Multimodal remote sensing image matching is challenging due to nonlinear radiative differences. This paper proposed an effective high-precision matching method based on Log-Gabor filtering, which including two stages: coarse matching and fine matching. In the coarse matching stage, key feature points are detected and initially matched using a phase congruency model and log-Gabor filters;in the fine matching stage, based on the estimated affine transformation model, the matching effect is further optimized using log-Gabor based template matching and three-dimensional phase correlation techniques. The experimental comparison of the algorithm studied in this paper with other image recognition algorithms shows that this algorithm has a significant performance advantage in terms of the number of correct matches and matching precision. © 2025 SPIE.

关键词： Template matching

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

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International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. Rajakumar, R. Krichen, Moez Ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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Approximation Schemes for Geometric Knapsack for Packing Spheres and Fat Objects 51

Approximation Schemes for Geometric Knapsack for Packing Sph...

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Acharya, Pritam Bhore, Sujoy Gupta, Aaryan Khan, Arindam Mondal, Bratin Wiese, Andreas Department of Mathematics Indian Institute of Science Education and Research Pune India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Mathematics Technical University of Munich Germany

ISBN: (纸本)9783959773225

We study the geometric knapsack problem in which we are given a set of d-dimensional objects (each with associated profits) and the goal is to find the maximum profit subset that can be packed non-overlappingly into a given d-dimensional (unit hypercube) knapsack. Even if d = 2 and all input objects are disks, this problem is known to be NP-hard [Demaine, Fekete, Lang, 2010]. In this paper, we give polynomial time (1 + Ε)-approximation algorithms for the following types of input objects in any constant dimension d: disks and hyperspheres, a class of fat convex polygons that generalizes regular k-gons for k ≥ 5 (formally, polygons with a constant number of edges, whose lengths are in a bounded range, and in which each angle is strictly larger than π/2), arbitrary fat convex objects that are sufficiently small compared to the knapsack. We remark that in our PTAS for disks and hyperspheres, we output the computed set of objects, but for a OΕ(1) of them we determine their coordinates only up to an exponentially small error. However, it is not clear whether there always exists a (1 + Ε)-approximate solution that uses only rational coordinates for the disks’ centers. We leave this as an open problem which is related to well-studied geometric questions in the realm of circle packing. © Pritam Acharya, Sujoy Bhore, Aaryan Gupta, Arindam Khan, Bratin Mondal, and Andreas Wiese.

关键词： Approximation algorithms

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Context-Aware Clinical Diagnosis Prediction via Hierarchical Ontology Representation 1

Context-Aware Clinical Diagnosis Prediction via Hierarchical...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Zhu, Bolin Liu, Chen Wang, Jiaojiao Li, Xin School of Computer Science & Technology Beijing Institute of Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350358780

In recent years, the extensive utilization of electronic medical records has led to the preservation of a large amount of historical patient information, culminating in the formation of Electronic Health Record (EHR) dataset. Through technologies such as deep learning and representation learning, EHR dataset has been employed for tasks in the medical field, such as disease prediction. However, the challenges of how to better learn the embeddings of medical codes and how to effectively model historical visit sequences remain significant issues in the field. Therefore, we introduce a novel disease prediction methodology grounded on hierarchical ontology representation and disease evolution. First, we incorporate medical ontology to obtain external prior knowledge and simultaneously utilize the co-occurrence relationships between diseases to construct a disease co-occurrence graph. By integrating information from both sources, we achieve the final embedding of the medical codes. Building on this foundation, we delve deeper into exploring the evolution and changes in diseases, specifying different sequence modeling methods, thereby acquiring the embeddings of the patients. The application of medical ontology is beneficial in mitigating data inadequacy, while the exploration of dynamic disease evolution enhances the effectiveness of predictive models. Experimental results on the MIMIC-III dataset indicate that the proposed method achieved superior performance in disease pre-diction tasks, thus validating the effectiveness of this approach. © 2023 IEEE.

关键词： Forecasting

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Research and Analysis of the Method for Measuring the Moisture Content of Cottonmaterials at Ultrahigh Frequencies

Research and Analysis of the Method for Measuring the Moistu...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Turkmenov, Khasan E. Ubaydullayeva, Shakhnoza R. Nikolov, Nikola N. Alexandrova, Mariela I. 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria

ISBN: (纸本)9798350312911

The article provides a theoretical analysis of the ultrahigh frequency (microwave) method of humidity measurement, discusses the problems of instrumentation of the method in question, and proposes a scheme of an experimental installation. Bulk cotton materials were selected as the objects of research. The electromagnetic wave radiation is analyzed during measurement and the evaluation of electrophysical characteristics requires the study of the functional dependence of the amplitude and phase shift on the mass ratio of humidity, mathematical models for various bulk cotton materials describing the joint effect of humidity, material density on the attenuation and phase shift of the passing wave are obtained. © 2023 IEEE.

关键词： Humidity control

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TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering 39

TPCH: Tensor-interacted Projection and Cooperative Hashing f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Zhongwen Li, Xingfeng Sun, Yinghui Sun, Quansen Sun, Yuan Ling, Han Dai, Jian Ren, Zhenwen School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Engineering Southeast University China College of Computer Science Sichuan University China Southwest Automation Research Institute China South Industries Group Corporation China School of National Defence Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)157735897X

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in low-dimensional spaces, clustering performance, and sensitivity to noise. To address this issue, we propose a novel approach named Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering(TPCH). TPCH stacks multiple projection matrices into a tensor, taking into account the synergies and communications during the projection process. By capturing higher-order multi-view information through dual projection and Hamming space, TPCH employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate that this refined method significantly outperforms state-of-the-art methods in clustering on five large-scale multi-view datasets. Moreover, in terms of CPU time, TPCH achieves substantial acceleration compared to the most advanced current methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sorting

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Dual Critic Reinforcement Learning under Partial Observability 38

Dual Critic Reinforcement Learning under Partial Observabili...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Jinqiu Zhao, Enmin Wei, Tong Xing, Junliang Xiang, Shiming Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Computer Science and Technology Tsinghua University China

Partial observability in environments poses significant challenges that impede the formation of effective policies in reinforcement learning. Prior research has shown that borrowing the complete state information can enhance sample efficiency. This strategy, however, frequently encounters unstable learning with high variance in practical applications due to the over-reliance on complete information. This paper introduces DCRL, a Dual Critic Reinforcement Learning framework designed to adaptively harness full-state information during training to reduce variance for optimized online performance. In particular, DCRL incorporates two distinct critics: an oracle critic with access to complete state information and a standard critic functioning within the partially observable context. It innovates a synergistic strategy to meld the strengths of the oracle critic for efficiency improvement and the standard critic for variance reduction, featuring a novel mechanism for seamless transition and weighting between them. We theoretically prove that DCRL mitigates the learning variance while maintaining unbiasedness. Extensive experimental analyses across the Box2D and Box3D environments have verified DCRL's superior performance. © 2024 Neural information processing systems foundation. All rights reserved.

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Contrastive Disentangled Learning on Graph for Node Classification 13

Contrastive Disentangled Learning on Graph for Node Classifi...

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13th IEEE International Conference on CYBER technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Zhang, Xiaojuan Fu, Jun Li, Shuang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350315196

Contrastive learning methods have attracted considerable attention due to their remarkable success in analyzing graph-structured data. Inspired by the success of contrastive learning, we propose a novel framework for contrastive disentangled learning on graphs, employing a disentangled graph encoder and two carefully crafted self-supervision signals. Specifically, we introduce a disentangled graph encoder to enforce the framework to distinguish various latent factors corresponding to underlying semantic information and learn the disentangled node embeddings. Moreover, to overcome the heavy reliance on labels, we design two self-supervision signals, namely node specificity and channel independence, which capture informative knowledge without the need for labeled data, thereby guiding the automatic disentanglement of nodes. Finally, we perform node classification tasks on three citation networks by using the disentangled node embeddings, and the relevant analysis is provided. Experimental results validate the effectiveness of the proposed framework compared with various baselines. © 2023 IEEE.

关键词： Embeddings

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Few-Shot Adversarial Prompt Learning on Vision-Language Models 38

Few-Shot Adversarial Prompt Learning on Vision-Language Mode...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Yiwei Xia, Xiaobo Lin, Zhiwei Han, Bo Liu, Tongliang School of Automation Beijing Institute of Technology China Sydney AI Centre University of Sydney Australia Department of Computer Science Hong Kong Baptist University Hong Kong

The vulnerability of deep neural networks to imperceptible adversarial perturbations has attracted widespread attention. Inspired by the success of vision-language foundation models, previous efforts achieved zero-shot adversarial robustness by aligning adversarial visual features with text supervision. However, in practice, they are still unsatisfactory due to several issues, including heavy adaptation cost, suboptimal text supervision, and uncontrolled natural generalization capacity. In this paper, to address these issues, we propose a few-shot adversarial prompt framework where adapting input sequences with limited data makes significant adversarial robustness improvement. Specifically, we achieve this by providing adversarially correlated text supervision that is end-to-end learned from adversarial examples. We also propose a novel training objective that enhances the consistency of multi-modal features while encourages differentiated uni-modal features between natural and adversarial examples. The proposed framework gives access to learn adversarial text supervision, which provides superior cross-modal adversarial alignment and matches state-of-the-art zero-shot adversarial robustness with only 1% training data. Code is available at: https://***/lionel-w2/FAP. © 2024 Neural information processing systems foundation. All rights reserved.

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