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arXiv 2024年

作者： Liu, Kangdao Zeng, Hao Huang, Jianguo Zhuang, Huiping Vong, Chi-Man Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China Department of Computer and Information Science University of Macau China College of Computing and Data Science Nanyang Technological University Singapore Shien-Ming Wu School of Intelligent Engineering South China University of Technology China

Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classifier for maximum predictive efficiency, Conformal Training rectifies the training objective with a regularization that minimizes the average prediction set size at a specific error rate. However, the regularization term inevitably deteriorates the classification accuracy and leads to suboptimal efficiency of conformal predictors. To address this issue, we introduce Conformal Adapter (C-Adapter), an adapter-based tuning method to enhance the efficiency of conformal predictors without sacrificing accuracy. In particular, we implement the adapter as a class of intra order-preserving functions and tune it with our proposed loss that maximizes the discriminability of non-conformity scores between correctly and randomly matched data-label pairs. Using C-Adapter, the model tends to produce extremely high non-conformity scores for incorrect labels, thereby enhancing the efficiency of prediction sets across different coverage rates. Extensive experiments demonstrate that C-Adapter can effectively adapt various classifiers for efficient prediction sets, as well as enhance the conformal training method. Copyright © 2024, The Authors. All rights reserved.

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Triplet Attention-Enhanced UNet Architectures for Advanced Skin Lesion Segmentation

Triplet Attention-Enhanced UNet Architectures for Advanced S...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Zaid Al-Huda Mugahed A. Al-Antari Riyadh Nazar Ali Algburi Omar Al-Maqtari Taha M Rajeh Ghufran Ahmad Khan Stirling College Chengdu University Chengdu P. R.China Department of Artificial Intelligence and Data Science College of AI Convergence Sejong University Seoul Korea Aeronautical Technical engineering College of Technical Engineering Al-Farahidi University Baghdad Iraq School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu P. R. China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

ccurate segmentation of skin lesions is critical the early detection and successful treatment of skin cancer, particularly melanoma, which has a significant impact on patient survival rates. This paper investigates a novel approach to skin lesion segmentation by integrating the Triplet Attention mechanism into UNet-based architectures, aiming to enhance the precision and reliability of automated segmentation in der-moscopic images. The proposed model leverages the strengths of both UNet’s encoder-decoder structure and the attention mechanism’s ability to focus on critical features while suppressing irrelevant background noise. Additionally, an Adaptive Weighted Loss function is introduced to tackle the class imbalance often present in skin lesion datasets, thereby enhancing segmentation performance. Extensive experiments were conducted using publicly available datasets, demonstrating significant improvements in segmentation accuracy and robustness, particularly in challenging cases with unclear lesion boundaries and varying illumination conditions. Our results indicate that the Triplet Attention-Enhanced UNet model, combined with the Adaptive Weighted Loss function, outperforms existing methods, providing a more reliable tool for dermatologists in the early diagnosis and treatment planning of skin cancer.

关键词： Adaptation models Image segmentation Attention mechanisms Accuracy Computational modeling computer architecture Semisupervised learning Skin Robustness Lesions

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Lane Structure Fitting via Row-wise Grid Classification 7

Lane Structure Fitting via Row-wise Grid Classification

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7th International Conference on computer and Communications, ICCC 2021

作者： Liu, Yingying Huang, Tingpei Chen, Haihua Zhang, Yucheng Sun, Chen Li, Xiaoting China University of Petroleum College of Oceanography and Space Informatics Qingdao China China University of Petroleum College of Computer Science and Technology Qingdao China Chinese Academy of Sciences Institute of Computing Technology Beijing China Chinese Academy of Sciences Engineering Laboratory of Intelligent Agricultural Machinery Equipment Beijing China

ISBN: (纸本)9781665409506

With people's attention to driving safety, the development of Advanced Driving Assistance System (ADAS) is becoming more and more mature, and lane detection is a vital part of it. Although most of the lane detection work based on deep learning shows excellent performance, in practical application, we should pursue a better balance between accuracy and speed. Shadow, blur and sparse supervision are still the challenges of lane detection. In this paper, we design a light convolutional neural network (CNN) based on ResNet-18, which achieves the fastest running time under the benchmark of ensuring detection accuracy. Different from the segmentation based method, we use the row-wise grid classification method, which can greatly reduce the amount of calculation. In addition, we propose a loss function with curve fitting function as reference value, which can maintain the lane structure and improve the problem of no visual information. Our model is tested on the TuSimple and CULane datasets. The experimental results show that our method is astonishing in accuracy and speed compared with the existing state-of-the-art methods. © 2021 IEEE.

关键词： Curve fitting

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Intelligent Bandwidth Planner Enhancing Learning Technologies in Constrained Distance Learning Environments – a Pandemic Response

Intelligent Bandwidth Planner Enhancing Learning Technologie...

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International Conference on Information Technology Based Higher Education and Training, ITHET

作者： Prince Yaw Owusu Amoako Isaac O. Osunmakinde School of Computing College of Science Engineering and Technology University of South Africa Pretoria South Africa Computer Science Department College of Science Engineering and Technology Norfolk State University Norfolk Virginia USA

ISBN: (纸本)9781665489096

Bandwidth resource in open distance electronic learning platform is scarce when services performed by many users contend for bandwidth, causing congestion in the network. The challenge has increased tremendously since almost all academic institutions perform activities online during the pandemic. This paper investigates the inherent workload constraints among open-distance electronic learning (ODeL) services competing for scarce resources and intends to forecast future bandwidth demands to prevent online class disruptions. A predictive framework of bandwidth management, which integrates a sustainable hidden Markov model (HMM) and a normalization policy, coupled with SolarWinds technology for prior network data feeder, is developed. A sustainable HMM $\alpha$ emerges from three HMM candidates based on test priorities on bandwidth demands. Compared to four popular methods, detailed experiments on the proposed model revealed performance analysis of error metrics below 0.5 at peak and off-peak periods. The emerged HMM $\alpha$ reliably predicted bandwidth capacities required to sustain the competing ODeL services with an accuracy of 94%.

关键词： Training Measurement computer aided instruction Electronic learning Pandemics Hidden Markov models Bandwidth

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Analysis of the Requirement and Artificial Intelligence-based Resource Management System in Cloud

Analysis of the Requirement and Artificial Intelligence-base...

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International Conference on Advanced computing and Communication Systems (ICACCS)

作者： S. Nagasundaram B. N. Bobinath Ashwini Shedthi K. Rajalakshmi Trupti Dandekar Humnekar Millerjothi School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai TamilNadu India Department of Computer Science and Engineering NPR College of Engineering Dindigul TamilNadu India Department of Commerce and Management New Horizon College Marathahalli and Research Scholar at Amrita University Bangalore India Department of Marketing CMS Business School Jain (Deemed to be) University Bangalore India Department of Mechanical Sri Eshwar College of Engineering Coimbatore India

Artificial Intelligence (AI) approaches, such as the big data, mobile computing, and Internet of Things (IoT) have become more common in the computer science area in recent years. It is critical to properly manage resources in order to maintain service quality, service level agreements, and the system's overall availability. AI may be used to address the needs of cloud resource management, and this study examines and evaluates several approaches. Fog computing systems, intelligent cloud computing systems and edge-cloud systems are all types of AI-based cloud resource management systems that have been reviewed in this contribution. In this research, an intelligent resource management method that controls mobile resources by monitoring device states and projecting their future stability is proposed. We look at the possibility of using our suggested resource management system to a variety of cloud-based platforms.

关键词： Cloud computing Stability analysis Mobile handsets Virtual machining Resource management Internet of Things Reliability

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Asym-Unet: An Asymmetric U-Shape Network for Lesion Segmentation of Breast Cancer

SSRN

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SSRN 2024年

作者： Liu, Jia Shao, Jun Xu, Sen Tang, Zhiyong Liu, Weiquan Li, Zeshuai Wang, Tao Bian, Xuesheng Department of Information Engineering Yancheng Institute of Technology Jiangsu Yancheng224007 China Fujian Key Lab of Sensing and Computing for Smart Cities School of Informatics Xiamen University Fujian Xiamen361000 China Department of Ultrasound The First People’s Hospital of Yancheng Jiangsu Yancheng224007 China Department of Electrical Engineering Shanxi Electric Power Technical College Shanxi Taiyuan030020 China

Precise segmentation of breast ultrasound images is crucial in early breast cancer screening. However, the diverse tumor morphology, blurred boundaries, and the similar grey intensity distribution between lesions and normal tissues in breast ultrasound images make it challenging to segment lesions accurately. To tackle these issues, we propose the Asym-Unet, an asymmetric U-shaped network, to segment the breast lesion in ultrasound images accurately and reliably. In detail, our Asym-Unet consists of a multi-branch residual encoder, an external attention module, and a boundary detection module. The multi-branch residual encoder effectively captures the discriminative representation of breast lesions for subsequent segmentation tasks. The external attention module further guides the network to focus on the lesion areas while filtering out interference information for diagnosis. Additionally, to further improve the network's performance, we introduce the boundary detection module for refining the boundary of the predicted masks. The boundary detection module offers extra supervision and guidance through a multi-level prediction layer. We compared our Asym-Unet with ten other segmentation methods on two publicly available datasets. The results demonstrate that the network performs state-of-the-art in the breast tumor segmentation task. © 2024, The Authors. All rights reserved.

关键词： Diagnosis

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KANsformer for Scalable Beamforming

arXiv

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arXiv 2024年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit The School of Computer and Technology Beijing Jiaotong University Beijing100044 China The Institute of Communications Engineering Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan The School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating transformer and Kolmogorov–Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input-single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms existing benchmark DL approaches, and is adaptable to the change in the number of mobile users with real-time and near-optimal inference. © 2024, CC BY.

关键词： Budget control

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Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

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computers, Materials & Continua 2021年第8期68卷 2693-2709页

作者： Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam ShuiHua Wang University of Wah Wah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan COMSATS University Islamabad Wah CampusWah CanttPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics University of LeicesterLeicesterUK

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.

关键词： Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet

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Incorporating a Decision Support Approach within the Agile Mobile Application Development Process 3

Incorporating a Decision Support Approach within the Agile M...

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3rd International Conference on computer Applications and Information Security, ICCAIS 2020

作者： Aljuhani, Abdulmajeed Alhubaishy, Abdulaziz Taibah University Faculty of Computer Science and Engineering Medina Saudi Arabia Saudi Electronic University College of Computing and Informatics Medina Saudi Arabia

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142128

According to the literature, traditional desktop-based application development processes profit from the adoption of Multi-Criteria Decision-Making (MCDM) approaches under specific conditions. However, agile methodology often shuns this method, instead encouraging a cooperative decision support structure. Within the domain of mobile-based applications, an optimized structure is constitutionally introduced, thus, the presentation of a powerful decision-making approach in agile mobile application development should prompt a more significant level of contentment with mobile applications built in this way. The present paper introduces an approach to adopting the MCDM method for the agile mobile application development process. The paper consists of a framework for investigating insertion points for decision-making method - namely, the Best- Worst Method (BWM) - and a practical example of the application of the Best-Worst decision support method in the development process of agile mobile applications. © 2020 IEEE.

关键词： Decision making

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How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting Governance

arXiv

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arXiv 2023年

作者： Li, Chao Palanisamy, Balaji Xu, Runhua Duan, Li Liu, Jiqiang Wang, Wei Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh United States School of Computer Science and Engineering Beihang University Beijing China

Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the original Steem community to leave the blockchain that they maintained for years. This is a historical event in the evolution of blockchains and Web 3.0. Despite its significant disruptive impact, little is known about how vulnerable DPoS blockchains are in general to takeovers and the ways in which we can improve their resistance to takeovers. In this paper, we demonstrate that the resistance of a DPoS blockchain to takeovers is governed by both the theoretical design and the actual use of its underlying coin-based voting governance system. When voters actively cooperate to resist potential takeovers, our theoretical analysis reveals that the current active resistance of DPoS blockchains is far below the theoretical upper bound. However in practice, voter preferences could be significantly different. This paper presents the first large-scale empirical study of the passive takeover resistance of EOSIO, Steem and TRON. Our study identifies the diversity in voter preferences and characterizes the impact of this diversity on takeover resistance. Through both theoretical and empirical analyses, our study provides novel insights into the security of coin-based voting governance and suggests potential ways to improve the takeover resistance of any blockchain that implements this governance model. © 2023, CC BY-NC-ND.

关键词： Blockchain

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