检索结果-内蒙古大学图书馆

Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

Neural computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Prompt Customization for Continual Learning

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1373-1385页

作者： Dai, Yong Hong, Xiaopeng Wang, Yabin Ma, Zhiheng Jiang, Dongmei Wang, Yaowei Shenzhen Polytechnic University School of Artificial Intelligence Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Harbin Institute of Technology Faculty of Computing Harbin150001 China Xi’an Jiaotong University School of Electronic and Information Engineering Xiaan710049 China Shenzhen University of Advanced Technology Shenzhen518071 China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518071 China Harbin Institute of Technology School of Computing Shenzhen518055 China

Contemporary continual learning approaches typically select prompts from a pool, which function as supplementary inputs to a pretrained model. However, this strategy is hindered by the inherent noise of its selection approach when handling increasing tasks. In response to these challenges, we reformulate the prompting approach for continual learning and propose the prompt customization (PC) method. PC mainly comprises a prompt generation module (PGM) and a prompt modulation module (PMM). In contrast to conventional methods that employ hard prompt selection, PGM assigns different coefficients to prompts from a fixed-sized pool of prompts and generates tailored prompts. Moreover, PMM further modulates the prompts by adaptively assigning weights according to the correlations between input data and corresponding prompts. We evaluate our method on four benchmark datasets for three diverse settings, including the class, domain, and task-agnostic incremental learning tasks. Experimental results demonstrate consistent improvement (by up to 16.2%), yielded by the proposed method, over the state-of-the-art (SOTA) techniques. The code has been released online. © 2020 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Surface-Continuous Scene Representation for Light Field Depth Estimation via Planarity Prior

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 5051-5066页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cui, Zhenglong Cong, Ruixuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou International Innovation Institute Zhejiang Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences China

Light field (LF) imaging captures both spatial and angular information of the real world, enabling precise depth estimation. However, images are merely discrete expressions of scenes. Limited by imaging technology, LF camera cannot capture the infinite rays emitted by scenes, leading to the discrete information storage (e.g. pixel). Consequently, previous deep learning methods have encountered challenges in accurately extracting depth information from LF images. In this paper, we investigate a surface-continuous scene representation using planarity prior and design PlaneNet, a Plane-based Network that successfully generates highly detailed depth maps for real scenes. Specifically, inspired by the plane assumption that real-world scenes generally yield piecewise smooth surfaces, we refine it to the pixel level for continuous surface approximation, which can overcome the limitations of discrete representation. Rather than explicitly parameterizing planes as multiple coefficients, we propose a novel plane regular sampling operator (PRSO), enabling the network to fit smooth depth surfaces easily. To explore the role of our theory at the feature level, we also introduce PRSO into the intermediate layers of PlaneNet. Experiments show that our method achieves state-of-the-art performance on both synthetic and real-world LF scenes, ranking 1st (MSE) on the HCI 4D Light Field benchmark. Furthermore, we explore the utilization of our representation in multiple LF depth estimation networks, and experiments demonstrate improved performance when surface-continuous representation is applied. © 1991-2012 IEEE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Toward a Cognitive Data Model: Exploring a Mind-Inspired Approach to Database Design

arXiv

引用

arXiv 2025年

作者： Pieris, Dhammika Department of Information Systems Engineering and Informatics Faculty of Computing University of Sri Jayawardenepura Sri Lanka

The Cognitive Data Model (CDM) is proposed. A novel approach to database design, inspired by the belief that the human brain operates with a logical data model independent of its anatomical structure. The study aims to identify and replicate this CDM to enhance database design, resolving limitations of the existing models in handling complex relationships, scalability, and adaptability. The methodology involves empirical observation, cognitive experiments, iterative modelling and critical thinking. Findings suggest that the information processing in the brain occurs sequentially with forming associations on atomic static data in a pairwise, time-dependent manner. This insight led to the development of a meta-framework for the brain’s information processing to design CDM based on it. The CDM offers improved data modelling and database design, efficient querying, and potential applications in AI, machine learning, and big data analytics. Future research will focus on formalizing the model, implementing it in 3D environments, and developing a query language. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

Automated Classification and Segmentation of Brain Tumor Images using Backboned ResUNet Framework 6

Automated Classification and Segmentation of Brain Tumor Ima...

引用

6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prasanalakshmi, B. Anitha, Cuddapah Balamanigandan, R. Mahaveerakannan, R. Gade, N.V. Ratnakishor Adjunct Faculty Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentation since they perform well in automated tumor segmentation using tumor scans. Due to the complexity, training deeper neural networks usually takes a long time and many processing resources. An automated approach for classifying and segmenting images of Brain tumor MRI is proposed. As a hybrid transfer learning approach, modified ResNet50 identifies the class of MRI images as Benign or malignant. After identifying the Benign images, the tumor’s location is segmented using a hybrid network involving ResUNet with the Xception network as Backbone. This combination approach is chosen after its comparative analysis with other backbones. Various combination approaches are carried out, and performance metrics like Jaccard, Dice, and Tversky coefficient are done to prove the ResUNet backboned with Xception plays a better role concerning accuracy and speed. The proposed approach was tested on the dataset Cancer Imaging Archive to validate its efficiency. The proposed model has an average dice similarity coefficient score of 0.94, outperforming the conventional transfer learning training method with all the layers. © 2025 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Automating Prompt Leakage Attacks on Large Language Models Using Agentic Approach

arXiv

引用

arXiv 2025年

作者： Sternak, Tvrtko Runje, Davor Granoša, Dorian Wang, Chi Faculty of Electrical Engineering and Computing University of Zagreb Croatia SplxAI Google DeepMind United Kingdom

This paper presents a novel approach to evaluating the security of large language models (LLMs) against prompt leakage—the exposure of system-level prompts or proprietary configurations. We define prompt leakage as a critical threat to secure LLM deployment and introduce a framework for testing the robustness of LLMs using agentic teams. Leveraging AG2 (formerly AutoGen), we implement a multi-agent system where cooperative agents are tasked with probing and exploiting the target LLM to elicit its prompt. Guided by traditional definitions of security in cryptography, we further define a prompt leakage-safe system as one in which an attacker cannot distinguish between two agents: one initialized with an original prompt and the other with a prompt stripped of all sensitive information. In a safe system, the agents’ outputs will be indistinguishable to the attacker, ensuring that sensitive information remains secure. This cryptographically inspired framework provides a rigorous standard for evaluating and designing secure LLMs. This work establishes a systematic methodology for adversarial testing of prompt leakage, bridging the gap between automated threat modeling and practical LLM security. You can find the implementation of our prompt leakage probing on GitHub: https://***/sternakt/prompt-leakage-probing © 2025, CC BY-NC-ND.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Template Matching in Images using Segmented Normalized Cross-Correlation

arXiv

引用

arXiv 2025年

作者： Marušić, Davor Popović, Siniša Kalafatić, Zoran Independent Researcher Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

In this paper, a new variant of an algorithm for normalized cross-correlation (NCC) is proposed in the context of template matching in images. The proposed algorithm is based on the precomputation of a template image approximation, enabling more efficient calculation of approximate NCC with the source image than using the original template for exact NCC calculation. The approximate template is precomputed from the template image by a split-and-merge approach, resulting in a decomposition to axis-aligned rectangular segments, whose sizes depend on per-segment pixel intensity variance. In the approximate template, each segment is assigned the mean grayscale value of the corresponding pixels from the original template. The proposed algorithm achieves superior computational performance with negligible NCC approximation errors compared to the well-known Fast Fourier Transform (FFT)-based NCC algorithm, when applied on less visually complex and/or smaller template images. In other cases, the proposed algorithm can maintain either computational performance or NCC approximation error within the range of the FFT-based algorithm, but not both. © 2025, CC BY-NC-SA.

关键词： Image matching

来源：评论

学校读者我要写书评

暂无评论

A Unified Hypergraph Framework for Inter and Intra-Session Dynamics in Session-Based Social Recommendations

引用

IEEE Transactions on Big Data 2025年

作者： Khan, Bilal Wu, Jia Yang, Jian Hayat, Malik Khizar Xue, Shan The School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Session-based recommendations have become increasingly important in social media platforms due to the dynamic and temporal nature of user interactions. The utilization of Graph Neural Networks in these systems has grown due to their proficiency in incorporating node information and structural topology. However, current recommendation methods that use graphs focus on recommendations within a single session, neglecting the more complex dependencies between different sessions. This omission limits improvements in the accuracy of recommendations. In addition, existing GNN-based approaches generally focus on simple binary connections, neglecting to capture the intricate and heterogeneous interactions in real-world situations. Furthermore, a notable obstacle arises from the absence of node positional information for hyperedges in hypergraphs. Therefore, different item orders can lead to identical hyperedges, which limits the formation of precise session vector representations. The paper proposes a unified framework utilizing heterogeneous hypergraph neural networks for session-based social recommendations to address these limitations. This framework utilizes hypergraphs to depict complex multivariate connections among sessions, social networks, and items. It addresses the problem of hyperedge ambiguity while maintaining the sequential order of data. The methodology entails creating a linkage graph and a session-item graph, which aid in identifying similar user intentions across various sessions and potential behavior patterns within a single session. In addition, the framework utilizes a Graph Attention Network (GAT) to combine social information from users and their connections, thereby improving the representation of user interests. Empirical assessments on three datasets show that our proposed model outperforms popular recommendation models. This emphasizes its effectiveness in accurately capturing user preferences and behaviors in session-based social recommendations.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Flexible Privacy-Preserving Data computing With Bilateral Access Control for Cloud-Assisted IoT

引用

IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 3166-3178页

作者： Zhang, Yijian Chen, Jie Ning, Jianting Chu, Qiaohan Wang, Luping Zhang, Guang University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2500 Australia Wuhan University Institute for Math & AI Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science 430040 China East China Normal University Software Engineering Institute Shanghai200062 China Suzhou University of Science and Technology School of Electronic and Information Engineering Jiangsu215009 China

Cloud-assisted Internet of Things (IoT) is a new paradigm to compensate for the disadvantage of limited resources in IoT and extend the functional boundary of IoT. How to preserve the data privacy while identifying the data source in cloud-assisted IoT is a huge challenge and many cryptographic primitives such as matchmaking encryption (ME) are hence introduced to reach this goal. However, we observe that these tools fail to deal with some rigorous cases in which the data owner does not want anyone to disclose the entire data and even authorized users can only obtain a specified part. In this article, to address such issue, we design a privacy-preserving data computing system with bilateral access control (PDCS-BAC) for cloud-assisted IoT based on a new primitive called identity-based matchmaking functional encryption (IB-MFE). We give the formal definition and security model of IB-MFE, then present two concrete IB-MFE schemes for inner-product function. Thus, PDCS-BAC manages to simultaneously provide flexible privacy computation with enhanced data privacy, bilateral access control, and data authenticity. Finally, we conduct comprehensive performance evaluations to demonstrate the practicability of our schemes for cloud-assisted IoT. © 2004-2012 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Use of deep geothermal energy to supply heat to a village in Scotland

引用

Interactions 2025年第1期246卷 1-18页

作者： Penman, David Muhammad-Sukki, Firdaus Abu-Bakar, Siti Hawa Bani, Nurul Aini Mas’ud, Abdullahi Abubakar School of Computing Engineering & the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom Renewable Energy Research Laboratory Electrical Engineering Section British Malaysian Institute Universiti Kuala Lumpur Gombak53100 Malaysia Faculty of Artificial Intelligence Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Department of Electrical Engineering Jubail Industrial College Jubail Industrial City35718 Saudi Arabia

This paper explores the possibility of utilising geothermal energy in an existing district heat network in the Aberdeenshire village of Banchory to provide hot water through the creation of a hypothetical geothermal well. It considers a simulated deep geothermal single well (DGSW), commonly referred to as a coaxial system with a depth of 5000 m. The hypothetical well was created using the dimensions of existing oil wells which, once they have reached the end of their lifecycle, hold the potential to be repurposed for geothermal use. It was found that the thermal output of the well decreases over time due to the drop in local rock temperature which is a result of thermal extraction. Given this, the thermal output after a year of operation was calculated and found that, to directly supply the Banchory heat network, a volumetric flow rate of 4.8m3/hr was required. After a year of operation, the site's peak thermal production was 108.4 kW which equates to a production of more than 949.9 MWh over the first operational year. © The Author(s) 2025.

关键词： Geothermal wells

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：