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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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A Secure Framework for Authenticating and Controlling Access to IoT Devices Integrated with Blockchain

A Secure Framework for Authenticating and Controlling Access...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Sibgha Tahir Noshina Tariq Farrukh Aslam Khan Hilmand Khan Jalal Al-Muhtadi Department of Avionics Engineering Air University Islamabad Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan Center of Excellence in Information Assurance King Saud University Riyadh Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The Internet of Things (IoT) enables autonomous communication among devices, improving human life but posing security risks. The main challenges to IoT systems' security are unauthorized access, data tampering, and the inability of central access control mechanisms to cope with scalability and privacy. Therefore, robust authentication and access control mechanisms are crucial for secure data sharing across IoT devices. This paper combines Hyperledger Fabric (HLF) blockchain with IoT for enhanced access control to establish trust in authentication and identification of devices along with secure communication. Hyperledger components are implied for developing an Attribute-Based Access Control (ABAC) model, including certification authorities, endorsers, and membership service providers. The system is implemented using HLF source code, Docker images, and IBM Cloud Kubernetes services, with an interface via Node-Red for device registration, authentication, blockchain data storage, and ledger retrieval. Experimental results demonstrate effective attack mitigation and better efficiency in terms of time complexity and energy consumption.

关键词： Access control Cloud computing Power demand Distributed ledger Authentication Fabrics Blockchains Delays Internet of Things Time complexity

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yifan Zhang Haolong Xiang Xuyun Zhang Xiaolong Xu Wei Fan Qin Zhang Lianyong Qi School of software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia Uffield Department of Women's & Reproductive Health University of Oxford England College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering University of Petroleum (East China) China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

关键词： Learning systems Training Accuracy Clustering algorithms Forestry Network security Fraud Time complexity Parallel algorithms Anomaly detection

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S3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation

arXiv

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arXiv 2024年

作者： Wang, Gui Li, Yuexiang Chen, Wenting Ding, Meidan Cheah, Wooi Ping Qu, Rong Ren, Jianfeng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Group University Engineering Research Center of Digital Medicine and Healthcare Guangxi Medical University Guangxi Nanning China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong School of Computer Science University of Nottingham Nottingham United Kingdom

Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segmenting small lesions, as it occupies only a minor portion of an image, while down sampling operations may inevitably lose focus on local features of small lesions. To tackle the challenges, we propose a Small-Size-Sensitive Mamba (S3-Mamba), which promotes the sensitivity to small lesions across three dimensions: channel, spatial, and training strategy. Specifically, an Enhanced Visual State Space block is designed to focus on small lesions through multiple residual connections to preserve local features, and selectively amplify important details while suppressing irrelevant ones through channel-wise attention. A Tensor-based Cross-feature Multi-scale Attention is designed to integrate input image features and intermediate-layer features with edge features and exploit the attentive support of features across multiple scales, thereby retaining spatial details of small lesions at various granularities. Finally, we introduce a novel regularized curriculum learning to automatically assess lesion size and sample difficulty, and gradually focus from easy samples to hard ones like small lesions. Extensive experiments on three medical image segmentation datasets show the superiority of our S3-Mamba, especially in segmenting small lesions. Our code is available at https://***/ErinWang2023/S3-Mamba. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Joint Route-Reservations with Pricing ⁎

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IFAC-PapersOnLine 2021年第2期54卷 25-30页

作者： C. Menelaou S. Timotheou P. Kolios C.G. Panayiotou KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of Cyprus

This work investigates how pricing can enhance the operation and adoption of route-reservations. Route-reservations is a demand and traffic management mechanism that can alleviate congestion and significantly reduce travel times. In this work, route-reservations are integrated with a pricing mechanism used to provide alternative path choices of different prices. The proposed integration offers a more flexible framework in which users can make route and departure choices based on their current needs. The approach is evaluated using microscopic simulations, illustrating the substantial reduction in delays and travel times of the proposed pricing scheme.

关键词： Route-Reservations Control Optimization of Transportation Systems Pricing

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Optimal Economic Dispatch of Power System Networks with Renewable Energy Sources

Optimal Economic Dispatch of Power System Networks with Rene...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Atif M. Mohammad Osama A. Alghamdi Abdullah Ali Alhusssainy Muhyaddin Rawa Abdullah M. Abusorrah Y. A. Al-Turki Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia

This paper compares betwe!en different types of batteries of a grid-connected hybrid power system that utilizes renewable energy sources (PV and Wind Turbines). The hybrid system is located in Yanbu region in Saudi Arabia. Part of the vision of 2030 of Saudi Arabia is to use renewable energy sources to cover a portion of the total electricity production. The required energy to be produced by renewable energy sources is 850 MW for the vision of 2030. To meet such a requirement, a thorough study is carried out to investigate a potential solution. The purpose of this study is to design a hybrid system which contains battery storage and has the lowest cost. Ten different batteries were used for a comparison study to examine the hybrid system with the lowest cost. Hybrid Optimization of Multiple Energy Resources (HOMER) software was used to design the hybrid system. HOMER provides real time evaluation of power systems. The criteria used for decision making purpose are the net present cost (NPC), levelized cost of energy (COE), electricity production, initial capital cost, nominal battery capacity, and battery autonomy. The results indicate that the hybrid system with Zinc Bromine flow battery (electrochemical type) has the lowest cost.

关键词： Renewable energy sources Bromine Costs Production software Hybrid power systems Batteries

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An empirical study on the impact of class overlap in just-in-time software defect prediction 33

An empirical study on the impact of class overlap in just-in...

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33rd International Conference on software engineering and Knowledge engineering, SEKE 2021

作者： Yi, Minyang Fan, Guisheng Yu, Huiqun Yang, Xingguang Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China

ISBN: (纸本)1891706527

Just-in-time software defect prediction (JIT-SDP) is an active research topic in the field of software engineering, aiming at identifying defect-inducing code changes. Most of the current JIT-SDP work focused on model construction. It is often ignored that the performance of classifiers often depends on high quality data. In this paper, we first investigate the impact of the class overlap problem on the performance of the classifiers in JIT-SDP, and propose a new effective preprocessing method (IKMCCA-TL) combining improved K-Means clustering cleaning approach and Tomek-link method. In order to objectively estimate the impact of class overlap on the classifiers in JIT-SDP, we conduct a large-scale empirical study on the data sets of six open source projects and compare the performance of LR, RF and KNN classifiers by using IKMCCA or KMCCA or NCL and without cleaning data. Experimental results show that after removing overlapping instances, the performance of the classifiers is significantly improved in terms of balance, recall and AUC and our proposed method achieves the best performance. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： K-means clustering

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Reinforcement Learning Architecture for Facial Skin Treatment Recommender

Reinforcement Learning Architecture for Facial Skin Treatmen...

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International Conference on software engineering Research, Management and Applications (SERA)

作者： Jennifer Kim Jin Khalil Dajani Mira Kim Soo Dong Kim Bilal Khan Daniel Hyun Jin Computer Science and Engineering California State University San Bernardino CA Department of Computer Science California State University Fullerton CA USA School of Software Soongsil University Seoul Korea Department of Radiology Medical Center Loma Linda University Loma linda CA

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

In the domain of facial skin treatment, personalization of treatment methods and management of treatment effectiveness among users/patients become the key challenges. Our approach to addressing the challenges is to develop a treatment recommender by utilizing a policy of reinforcement learning and continuously optimizing the policy to learn the variability. We leverage algorithmic decision-making through the Reinforcement Learning (RL) model's policy to personalize recommendations according to the unique characteristics and treatment responses of each user. This RL-based recommender should provide a high performance of personalizing treatment recommendations and continue to learn user-specific effectiveness of treatment methods. Preliminary results demonstrate the system's capacity to provide targeted, effective skin care recommendations, significantly enhancing user satisfaction and adherence to treatments.

关键词： Decision making computer architecture Deep reinforcement learning Skin Recommender systems Optimization System implementation

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An Implementation of a Route Reservation Architecture ⁎

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IFAC-PapersOnLine 2021年第2期54卷 1-6页

作者： C. Makridis C. Menelaou S. Timotheou C.G. Panayiotou KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of Cyprus

Route Reservation Architecture seems to be a reliable and durable traffic congestion mitigation mechanism that can significantly reduce drivers’ travel-times. In this framework, a Route Reservation controller is responsible for coordinating drivers’ departure times and routes to follow so as to arrive at their destination at the earliest time. Despite the great efficiency of the Route Reservation Architecture, its real-life implementation is a significant challenge considering the size and the complexity of urban networks. This work investigates and develops all the modules required to realize the Route Reservation Architecture as a real-life traffic and demand management mechanism. In doing so, this work proposes all the needed communication protocols and algorithmic methods that ensure its fast and reliable application and addresses any architecture’s scalability issues. The developed modules are evaluated over a large-scale urban area where a microscopic simulation serves as a realistic transportation network.

关键词： Modeling Control Optimization of Transportation Systems

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Imperceptible and Reliable Adversarial Attack 1

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4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a "sparse" or "global" way. Since the number of pixels perturbed by the "sparse" method and the perturbation intensity of each pixel added by the "global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an "pixel-wise" method which is somewhere in between the "sparse" or "global" way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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