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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： He, Junyi Zhang, Di Li, Xishuo Zhou, Yuezhi Zhang, Yaoxue School of Software Engineering Beijing Jiaotong University China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Computer Science and Engineering Beihang University China Tsinghua University Department of Computer Science and Technology China

ISBN: (纸本)9781665494571

Ultra-dense mobile edge computing (MEC) is expected as an effective approach to provide services with ultra-low latency and massive connectivity, which is extremely important for delay-sensitive applications. In this paper, we study service instance (SI) placement for delay-sensitive applications in ultra-dense MEC, which decides where to place applications and the required data (programming codes and datasets). In general, the SI placement problem is a combinatorial optimization and usually does not have low-complexity optimal solutions. Existing centralized policies are difficult to implement due to the high complexity, especially when edge servers are densely deployed in the ultra-dense network (UDN). To design an approach that can make SI placement decisions quickly and efficiently, this paper investigates a distributed strategy for multi-user multi-server edge systems in UDN. We first formulate the SI placement problem as a non-linear combinatorial optimization, which is proven to be NP-hard to compute a centralized optimal solution. Then, we propose an uncooperative game to design a low-complexity distributed algorithm. We further analyze the properties of the game and prove the existence of pure strategy Nash Equilibrium. A polynomial-complexity distributed algorithm is proposed to get the Nash Equilibrium. Theoretical analysis shows the algorithm's convergence and the bound of the price of anarchy (PoA), which serves as a performance guarantee in the worst-case scenarios compared to the global optimum. Numerical results validate the convergence and near-optimal performance of the proposed distributed algorithm. © 2021 IEEE.

关键词： Performance evaluation Games Reinforcement learning Nash equilibrium Servers Distributed algorithms Ultra-dense networks

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Attention-guided CenterNet deep learning approach for lung cancer detection

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computers in Biology and Medicine 2025年 186卷 109613-109613页

作者： Dawood, Hussain Nawaz, Marriam Ilyas, Muhammad U. Nazir, Tahira Javed, Ali School of Computing Skyline University College Sharjah United Arab Emirates Department of Software Engineering University of Engineering and Technology-Taxila Punjab 47050 Pakistan School of Computer Science University of Birmingham Dubai United Arab Emirates Department of Software Engineering and Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection. The primary importance of this work lies in its innovative integration of ResNet-34 with an attention mechanism within the CenterNet architecture, addressing critical limitations identified in previous studies. By augmenting the base network with an attention mechanism, our framework offers improved feature extraction capabilities, enabling the model to learn relevant patterns associated with lung cancer amidst complex backgrounds and varying environmental conditions. This enhancement facilitates more accurate and interpretable predictions while reducing computational complexity and inference times. Through extensive experimental evaluations conducted on standard datasets, our proposed approach demonstrates promising results, highlighting its potential to advance the field of lung cancer detection and diagnosis. Specifically, we have acquired the precision, recall, and F1-Score of 99.89 %, 99.82 %, and 99.85 % on the LUNA-16 dataset, and 98.33 %, 98.02 %, and 98.17 % for the Kaggle data sample, respectively which is showing the efficacy of our approach. One limitation of the work is that it cannot effectively locate the samples with intense light variations. Therefore, future research work is focused on overcoming this challenge. © 2024 Elsevier Ltd

关键词： Lung cancer

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Decoding Sentiments: An Efficient Trans-Long Short Term Memory to Understand Viewer Reactions on Ad-Supported Video Contents 3

Decoding Sentiments: An Efficient Trans-Long Short Term Memo...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Comcast Software Development & Engineering in Content Discovery & Ai Department of Computer Science United States Troy University Montgomery Campus AL United States Dt Amtrak C & Rs Platform Services Department of Electrical Engineering United States University of South Alabama United States

ISBN: (纸本)9798350354348

Viewer reaction analysis in ad-supported video content focuses on the use of advanced techniques to understand and interpret the emotional responses expressed by viewers during ad viewing experiences. This analysis goes beyond simple positive or negative sentiment and delves into the nuances of viewer reactions, allowing for a more comprehensive understanding of the emotional impact of ads. With this information, advertisers can make data-driven decisions to optimize their advertising strategies, create more engaging content, and ultimately enhance the viewer experience. One of the challenges in sentiment analysis for viewer reaction analysis is the inherent subjectivity and variability of human emotions. Emotions can be complex and subjective, making it difficult to accurately capture and interpret them solely based on text or viewer feedback. Different individuals may perceive and express emotions differently, adding another layer of complexity to the analysis process. Addressing these challenges requires a combination of advanced deep learning model refinement. Overcoming these challenges can provide valuable insights into viewer reactions, enabling advertisers and content creators to create more engaging and impactful ad-supported video content. In this paper, an Artificial Intelligence (AI)-based sentiment analysis is introduced to analyze the viewer's reaction while watching the ad in the video content. Here, an efficient model named Trans-Long Short-Term Memory (Trans-LSTM) is developed to perform the sentiment analysis. The developed Trans-LSTM is helpful in supervising the emotional data in the online video content and identifying the popularity of the corresponding video. The suggested model effectively analyzes the reviewer's actions while watching all types of video content. Finally, the experimental analysis is performed to find the effectiveness of the developed sentiment analysis model via various metrics. © 2024 IEEE.

关键词： Video analysis

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CAMLD: Contrast-Agnostic Medical Landmark Detection with Consistency-Based Regularization

arXiv

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arXiv 2024年

作者： Salari, Soorena Harirpoush, Arash Rivaz, Hassan Xiao, Yiming Department of Computer Science and Software Engineering Concordia University Montréal Canada Department of Electrical and Computer Engineering Concordia University Montréal Canada

Anatomical landmark detection in medical images is essential for various clinical and research applications, including disease diagnosis and surgical planning. However, manual landmark annotation is time-consuming and requires significant expertise. Existing deep learning (DL) methods often require large amounts of well-annotated data, which are costly to acquire. In this paper, we introduce CAMLD, a novel self-supervised DL framework for anatomical landmark detection in unlabeled scans with varying contrasts by using only a single reference example. To achieve this, we employed an inter-subject landmark consistency loss with an image registration loss while introducing a 3D convolution-based contrast augmentation strategy to promote model generalization to new contrasts. Additionally, we utilize an adaptive mixed loss function to schedule the contributions of different sub-tasks for optimal outcomes. We demonstrate the proposed method with the intricate task of MRI-based 3D brain landmark detection. With comprehensive experiments on four diverse clinical and public datasets, including both T1w and T2w MRI scans at different MRI field strengths, we demonstrate that CAMLD outperforms the state-of-the-art methods in terms of mean radial errors (MREs) and success detection rates (SDRs). Our framework provides a robust and accurate solution for anatomical landmark detection, reducing the need for extensively annotated datasets and generalizing well across different imaging contrasts. Our code will be publicly available at https://***/HealthXLab/CAMLD. © 2024, CC BY-NC-ND.

关键词： Clinical research

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MSAGNet: Crowd Counting Network based on Multi-Scale Attention Grading 34

MSAGNet: Crowd Counting Network based on Multi-Scale Attenti...

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Pattern Recognition and Tracking XXXIV 2023

作者： Wu, Zhiwei Ji, Peirong Chen, Yan Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

In recent years, great progress has been made in the study of crowd counting. Although the crowd counting networks being proposed to solve different problems have achieved satisfactory counting results, the differences of crowd density and scale in the same scene still degrade the overall counting performance. In order to deal with this problem, we propose a Multi-Scale Attention Grading Crowd Counting Network (MSAGNet), which focuses on different crowd densities in the scene by attention mechanism and fuses multi-scale information to reduce scale differences. Specifically, the grading attention feature obtaining module focuses on different densities of people in the scene by attention mechanism, and adaptively assigns corresponding weights to different density regions. Dense regions are given more weights, allowing the model to focus more on that part making the training of that region more accurate and effective. In addition, the multi-scale density feature fusion module fuses the feature maps containing density information to generate the final feature maps. The obtained feature maps contain attention information at different scales, which are density mapped to obtain the estimated density maps. This method can focus on different density regions in the same scene, and simultaneously fuse multi-scale information and attention weight, which can effectively solve the problem of counting dense regions that is difficult to calculate. Extensive experiments on existing crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF-QNRF) show that our method can effectively improve the counting performance. © 2023 SPIE.

关键词： Grading

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Optimizing Generative AI Applications: A Comparative Study of Effective Prompting Techniques

Optimizing Generative AI Applications: A Comparative Study o...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Shivya Jyoti Moulik Tejpal Jothi K R Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Generative Artificial Intelligence (GenAI) has emerged as a pivotal technology across various industries, driving advancements in automation, decision-making, and content generation. This paper investigates the efficacy of the prompt engineering methods such as zero-shot, one-shot, and few-shot prompting—in optimizing GenAI systems for diverse applications. Through a comprehensive literature review and an empirical survey involving 13 use cases such as chatbots, content creation, and medical decision support, we evaluate the performance of these prompting methods. The findings reveal that few-shot prompting excels in complex tasks, while zero-shot and one-shot prompting are more effective for simpler tasks. These insights provide practical guidance for leveraging GenAI across different domains, contributing to the advancement of AI-driven solutions.

关键词： Surveys Pervasive computing Industries Decision support systems Social networking (online) Large language models Chatbots Prompt engineering Human resource management Systematic literature review

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Makeup Transfer Based On Laplacian Pyramid Network 34

Makeup Transfer Based On Laplacian Pyramid Network

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Pattern Recognition and Tracking XXXIV 2023

作者： Chen, Yan Gong, Wenhui Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University - Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

Makeup transfer aims to extract a specific makeup from a face and transfer it to another face, which can be widely used in portrait beauty, and cosmetics marketing. At present, existing methods can achieve the transfer of the entire facial makeup, but the quality of makeup transfer is not excellent because there may be a mismatch between the two images. In this paper, we propose a facial makeup transfer network based on the Laplacian pyramid, which can better preserve the facial structure from the source image and achieve high-quality transfer results. The model consists of three parts: makeup feature extraction, facial structure feature extraction, and makeup fusion. The makeup extraction part is used to extract the facial makeup from the reference image. And the facial structure feature extraction part is used to extract facial structure from the source image, in order to solve the loss of facial details when extracting facial structural features, we used the method based on Laplacian Pyramid. The makeup fusion part will fuse the facial makeup with facial structure features. Many experiments on the MT dataset have shown that this method can transfer makeup successfully without changing the original facial structure, and achieve advanced performance in various makeup styles. © 2023 SPIE.

关键词： Feature extraction

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Real-time crowd counting via mobile-friendly Vision Transformer network 34

Real-time crowd counting via mobile-friendly Vision Transfor...

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Pattern Recognition and Tracking XXXIV 2023

作者： Ji, Peirong Wu, Zhiwei Chen, Yan Alam, Mohammad S. Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China Department of Electrical Engineering and Computer Science Texas A&M University - Kingsville KingsvilleTX78363-8202 United States

ISBN: (数字)9781510661691

ISBN: (纸本)9781510661684

Accurate crowd counting in congested scenes remain challengeable in the trade-off of efficiency and generalization. For solving this issue, we propose a mobile-friendly solution for the network deployment in high response speed demand scenarios. In order to introduce the profound potential of global crowd representations to lightweight counting model, this work suggests a novel crowd counting aimed mobile vision transformers architecture (CCMTNet), which strives for enhancing the efficiency of the model universality in real-time crowd counting tasks on resource constrained computing devices. The framework of linear CNN network interpolation structure with self-attention blocks endows the model with the ability of local feature extraction and global high-dimensional crowd information processing with low computational cost. In addition, several experimental networks with different scales based on the proposed architecture are comprehensively verified to balance the accuracy loss as compressing the computing costs. Extensive experiments on three mainstream datasets for crowd counting tasks well demonstrate the effectiveness of this proposed network. Particularly, CCMTNet achieves the feasibility of reconciling the counting accuracy and efficiency in comparisons with traditional lightweight CNN networks. © 2023 SPIE.

关键词： Efficiency

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Using Multinomial Naive Bayes Machine Learning Method to Classify, Detect, and Recognize Programming Language Source Code 23

Using Multinomial Naive Bayes Machine Learning Method to Cla...

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Odeh, Ayman Hussien Odeh, Munther Odeh, Nada Science- College of Engineering Al Ain University Software Engineering and Computer Al Ain United Arab Emirates Oldenburg University Department of Mathematics and Science Oldenburg Germany

ISBN: (纸本)9798350320244

Processing programming languages are very similar to processing natural languages, especially high-level languages such as Python, Java, C#, C, C++, and others. Therefore, the natural language processing concepts can be applied as one of the most important branches of artificial intelligence in detecting, recognizing, and classification scripts written in different programming languages. The programming language script classification can be counted as a classical machine learning problem. This research aims to present a model using Multinomial Naïve Bayes (MNB) artificial intelligence algorithm to identify and classify the programming language used in writing the source code file provided as an input for the proposed model. A set of categorized files containing source codes will be used in training the proposed model, and then the model will be able to automatically detect and classify a new script into one of the already trained categories. The machine learning method called NB Multinomial will be used to implement this matter. This work is very important for Mufti-programming language editors such as Visual Studio Code, Notepad+, and others, where the user can paste the source code, and the editor will recognize the programming language automatically. © 2022 IEEE.

关键词： Machine learning

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Analyzing Digital Image Anti-Forensics Techniques and Countermeasures 27

Analyzing Digital Image Anti-Forensics Techniques and Counte...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Agarwal, Saurabh Kumar, Rajeev Devi, Abha Jung, Ki-Hyun Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of Blockchain Technology Research Lab Department of Computer Science and Engineering Delhi Technological University New Delhi110042 India Department of Software Convergence Andong National University Gyeongbuk36729 Korea Republic of College Uttar Pradesh Sikandrabad India

ISBN: (纸本)9791188428137

With the increasing use of digital media, digital forensics has become a crucial method for ascertaining authenticity and detecting manipulations in images. However, intentional anti-forensic manipulations to hide forensic clues have made verification more difficult. The efforts to develop countermeasures against anti-forensics have contributed to the evolution toward robust techniques in the field of anti-forensics;hence, the development of anti-forensic methods and their respective countermeasures will remain continuous. This work provides a scientometric analysis, a powerful tool for the study of the growth of research output in accordance with time and citation, author, organizational, and keyword co-occurrence analysis. It enhances state-of-the-art analysis by underlining highly influential authors, organizations, and works and stipulates knowledge gaps. This would consequently provide a platform for building a community where insights and inclusiveness among researchers, industry practitioners, and policymakers would all come together to guide their strategies and decisions for innovation in this dynamic field. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fake detection

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