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32nd ACM International Conference on Multimedia, MM 2024

作者： Deng, Ruoxi Yu, Bin Lu, Jinxuan Zhou, Caixia Chen, Zhao-Min Hu, Jie Wenzhou Institute of Data Research School of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China School of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Semantic edge detection (SED) is pivotal for the precise demarcation of object boundaries, yet it faces ongoing challenges due to the prevalence of low-quality labels in current methods. In this paper, we present a novel solution to bolster SED through the encoding of both language and image data. Distinct from antecedent language-driven techniques, which predominantly utilize static elements such as dataset labels, our method taps into the dynamic language content that details the objects in each image and their interrelations. By encoding this varied input, we generate integrated features that utilize semantic insights to refine the high-level image features and the ultimate mask representations. This advancement improves the quality of these features and elevates SED performance. Experimental evaluation on benchmark datasets, including SBD and Cityscape, showcases the efficacy of our method, achieving leading ODS F-scores of 79.0 and 76.0, respectively. Our approach signifies a notable advancement in SED technology by seamlessly integrating multimodal textual information, embracing both static and dynamic aspects. © 2024 ACM.

关键词： Convolutional neural networks

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An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

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computers, Materials & Continua 2024年第1期78卷 713-733页

作者： Yanmei Li Jinghong Tang Weiwu Ding Jian Luo Naveed Ahmad Rajesh Kumar School of Artificial Intelligence Chongqing University of TechnologyChongqing400054China Computer School China West Normal UniversityNanchong637009China Department of Computer Science Prince Sultan UniversityRiyadh11586Saudi Arabia Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China

Alzheimer’s disease(AD)is a complex,progressive neurodegenerative *** subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical *** this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention *** aim to improve the precision of AD diagnosis and facilitate its early ***,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging ***,an attention mechanism is incorporated to selectively focus on the salient features within the imaging *** upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated *** proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic *** system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.

关键词： Alzheimer’s disease channel attention Med-3D hot update

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BONGARD-OPENWORLD: FEW-SHOT REASONING FOR FREE-FORM VISUAL CONCEPTS IN THE REAL WORLD 12

BONGARD-OPENWORLD: FEW-SHOT REASONING FOR FREE-FORM VISUAL C...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wu, Rujie Ma, Xiaojian Zhang, Zhenliang Wang, Wei Li, Qing Zhu, Song-Chun Wang, Yizhou School of Computer Science Peking University China National Key Laboratory of General Artificial Intelligence BIGAI China School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China

We introduce Bongard-OpenWorld, a new benchmark for evaluating real-world few-shot reasoning for machine vision. It originates from the classical Bongard Problems (BPs): Given two sets of images (positive and negative), the model needs to identify the set that query images belong to by inducing the visual concepts, which is exclusively depicted by images from the positive set. Our benchmark inherits the few-shot concept induction of the original BPs while adding the two novel layers of challenge: 1) open-world free-form concepts, as the visual concepts in Bongard-OpenWorld are unique compositions of terms from an open vocabulary, ranging from object categories to abstract visual attributes and commonsense factual knowledge;2) real-world images, as opposed to the synthetic diagrams used by many counterparts. In our exploration, Bongard-OpenWorld already imposes a significant challenge to current few-shot reasoning algorithms. We further investigate to which extent the recently introduced Large Language Models (LLMs) and Vision-Language Models (VLMs) can solve our task, by directly probing VLMs, and combining VLMs and LLMs in an interactive reasoning scheme. We even conceived a neuro-symbolic reasoning approach that reconciles LLMs & VLMs with logical reasoning to emulate the human problem-solving process for Bongard Problems. However, none of these approaches manage to close the human-machine gap, as the best learner achieves 64% accuracy while human participants easily reach 91%. We hope Bongard-OpenWorld can help us better understand the limitations of current visual intelligence and facilitate future research on visual agents with stronger few-shot visual reasoning capabilities. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computational linguistics

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Task Offloading and Resource Allocation for ICVs in CFNs via Constrained MARL

Task Offloading and Resource Allocation for ICVs in CFNs via...

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2024 IEEE International Symposium on Product Compliance Engineering-Asia, ISPCE-AS 2024

作者： Wang, Enshu He, Jinbo Liu, Bingyi Shi, Haiyong Han, Weizhen Wu, Libing School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798331523008

Computing First Network (CFN) aims to enable unified scheduling of computing nodes across the network, positioning it as a promising paradigm for managing the computationally intensive tasks of Intelligent Connected Vehicles (ICVs). Given the highly dynamic vehicle network topology and uneven vehicle density, ICV tasks may fail due to limited computing resource on individual node. Thus, it is crucial for CFN to efficiently schedule I CV tasks and allocate resources to ensure their successful completion. However, existing task scheduling methods often struggle with large action spaces and tend to over-look the correlation between offloading and resource allocation decisions. Additionally, the high computing resource demands of ICV tasks require CFN to allocate resources within constrained range to ensure timely overall task completion. To address these challenges, we propose a Constrained and Action Represented Multi-Agent Reinforcement Learning (CARMARL) framework that generates reliable policies to meet the QoS requirements of I CV tasks while fully utilizing computing resources in CFN. Specifically, CARMARL maintains a Conditional Variational Autoencoder (CVAE) to reduce the action space and enable agents to learn the correlation between offloading and allocation decisions and a Reward Constrained Policy Optimization (RCPO) module to ensure resource allocation remains reasonableness. Extensive experiments demonstrate that CARMARL improves task completion rate by up to 25.2 % compared to all baselines. © 2024 IEEE.

关键词： Constrained optimization

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Multi-dataset Pose Estimation based on the Fusion of Multiple Models 24

Multi-dataset Pose Estimation based on the Fusion of Multipl...

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3rd International Conference on computer, artificial intelligence and Control Engineering, CAICE 2024

作者： Jin, Guanghao Wang, Jieying Wang, Yuqing Du, Hui Ma, Lei Song, Qingzeng School of Artificial Intelligence Beijing Polytechnic Beijing China School of Computer Science and Technology Tiangong University Tianjin China

ISBN: (纸本)9798400716942

To increase the accuracy of pose estimation on multiple datasets, this paper proposes a new pose estimation method based on the fusion of multiple models. This method consists of two main steps: firstly, a dataset classification by YoloV5 algorithm is performed that is to classify the dataset that may contain the testing samples;secondly, the key point detection is performed by using a deep learning model-based pose estimation that is trained on the predicted dataset. We evaluated the methods on three publicly datasets: Leeds Sports Pose (LSP) dataset, CrowdPose dataset and AnimalPose one. As the experimental results show, our method can achieve the scalability on multiple datasets while ensuring high accuracy. © 2024 ACM.

关键词： Classify the dataset Multiple datasets Multiple models Pose estimation

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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A System to Increase the Accuracy of Deep Learning based Classification by the Distribution of Labels 24

A System to Increase the Accuracy of Deep Learning based Cla...

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3rd International Conference on computer, artificial intelligence and Control Engineering, CAICE 2024

作者： Jin, Guanghao Wang, Yuqing Wang, Jieying Du, Hui Ma, Lei Song, Qingzeng School of Artificial Intelligence Beijing Polytechnic Beijing China School of Computer Science and Technology Tiangong University Tianjin China

ISBN: (纸本)9798400716942

In some deep learning-based applications, we can use the distribution of labels to increase the classification accuracy like the distribution of the labels in the corresponding application. This kind distribution can be obtained by the experience of human being while it is generally imprecise. Using validation set can be a solution to compute precise distribution of labels while this is affected by the application change. In this paper, we propose a novel system, which efficiently utilizes the experience of human being and validation set to improve the performance of classification models. Firstly, our method selects and trains an existing deep learning model on the training set. Then, these models can output the probability of labels on each testing sample. Secondly, our system utilizes the user input and the validation set, which is to get the precise distribution of labels in an application. Finally, the computed distribution of labels is selectively combined with the probability of labels on testing samples to get the final classification results. We selected some existing deep learning models on three public datasets to evaluate the models. As the evaluation results show, our system can improve the performance of classification tasks, which is obviously higher than state-of-art methods. © 2024 ACM.

关键词： Contrastive Learning

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PNAP-YOLO: An Improved Prompts-Based Naturalistic Adversarial Patch Model for Object Detectors

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Annals of data science 2025年第3期12卷 1-18页

作者： Li, Jun Shan, Chenwu Shen, Liyan Ren, Yawei Zhang, Jiajie School of Computer Science Beijing Information Science and Technology University Beijing100192 China Artificial Intelligence Security Innovation Team Beijing Information Science and Technology University Beijing100192 China

Detectors have been extensively utilized in various scenarios such as autonomous driving and video surveillance. Nonetheless, recent studies have revealed that these detectors are vulnerable to adversarial attacks, particularly adversarial patch attacks. Adversarial patches are specifically crafted to disrupt deep learning models by disturbing image regions, thereby misleading the deep learning models when added to into normal images. Traditional adversarial patches often lack semantics, posing challenges in maintaining concealment in physical world scenarios. To tackle this issue, this paper proposes a Prompt-based Natural Adversarial Patch generation method, which creates patches controllable by textual descriptions to ensure flexibility in application. This approach leverages the latest text-to-image generation model—Latent Diffusion Model (LDM) to produce adversarial patches. We optimize the attack performance of the patches by updating the latent variables of LDM through a combined loss function. Experimental results indicate that our method can generate more natural, semantically rich adversarial patches, achieving effective attacks on various detectors. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning

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An Overview of LoRa Localization Technologies

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computers, Materials & Continua 2025年第2期82卷 1645-1680页

作者： Huajiang Ruan Panjun Sun Yuanyuan Dong Hamid Tahaei Zhaoxi Fang Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Science and Engineering School of Mechanical and Electrical EngineeringShaoxing UniversityShaoxing312000China

Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.

关键词： LoRa localization IoT TDoA RSSI application hybrid models

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A New Alliance of Machine Learning and Quantum Computing: Concepts, Attacks, and Challenges in IoT Networks

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IEEE Internet of Things Journal 2025年第12期12卷 18865-18886页

作者： Rishiwal, Vinay Agarwal, Udit Yadav, Mano Tanwar, Sudeep Garg, Deepak Guizani, Mohsen MJP Rohilkhand University Department of Computer Science and Information Technology Bareilly India Bareilly College Bareilly Department of Computer Science India Nirma University Institute of Technology Department of Computer Science and Engineering Gujarat Ahmedabad382481 India SR University School of Computer Science and Artificial Intelligence Telangana- Warangal506371 India Abu Dhabi United Arab Emirates

The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining Quantum Computing with Machine Learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. Quantum Computing, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, quantum computing, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating quantum computing approaches with ML. It also addresses the challenges and limitations of integrating ML and quantum computing in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge. © 2025 IEEE.

关键词： Quantum computers

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