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2025 IEEE/CVF Winter Conference on Applications of computer vision, WACV 2025

作者： Chen, Boqi Zhu, Yuanzhi Ao, Yunke Caprara, Sebastiano Sutter, Reto Ratsch, Gunnar Konukoglu, Ender Susmelj, Anna Eth Zurich Department of Computer Science Switzerland Eth Ai Center Switzerland Eth Zurich Computer Vision Lab Switzerland Balgrist University Hospital Switzerland University of Zurich Switzerland

ISBN: (纸本)9798331510831

Single-source domain generalization (SDG) aims to learn a model from a single source domain that can generalize well on unseen target domains. This is an important task in computer vision, particularly relevant to medical imaging where domain shifts are common. In this work, we consider a challenging yet practical setting: SDG for cross-modality medical image segmentation. We combine causality-inspired theoretical insights on learning domain-invariant representations with recent advancements in diffusion-based augmentation to improve generalization across diverse imaging modalities. Guided by the 'intervention-augmentation equivariant' principle, we use controlled diffusion models (DMs) to simulate diverse imaging styles while preserving the content, leveraging rich generative priors in large-scale pretrained DMs to comprehensively perturb the multidimensional style variable. Extensive experiments on challenging cross-modality segmentation tasks demonstrate that our approach consistently outperforms state-of-the-art SDG methods across three distinct anatomies and imaging modalities. The source code is available at https://***/ratschlab/ICMSeg. © 2025 IEEE.

关键词： Image segmentation

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Enhancing the Zebra Optimization Algorithm with Chaotic Sinusoidal Map for Versatile Optimization

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第1期5卷 307-319页

作者： Anand, D. Khalaf, Osamah Ibrahim Chandra, G. Rajesh Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation GreenFileds A.P Vaddeswaram522302 India Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Department of Computer Science and Engineeirng KKR & KSR Institute of Technology and Sciences Andhra Pradesh India

In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA) is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibits great optimization capabilities, but the addition of CSM increases its potential even more. This addition greatly strengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks. CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions, including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM to investigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shown by CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatility through multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOA becomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficult optimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is a relatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership and following, in which members of the population—zebras in this case—cooperate to solve optimisation issues in the best possible ways. A chaotic sinusoidal map can improve the ZOA's exploitation and exploration capabilities, increasing its adaptability for optimisation across a range of problem *** success of CZOA and its potential as a potent © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Optimization

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Recombination Operators for the Multi-Objective Team Formation Problem in Social Networks 13

Recombination Operators for the Multi-Objective Team Formati...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Juárez, Julio Brizuela, Carlos A. Terashima-Marín, Hugo Coello Coello, Carlos A. School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Cicese Research Center Department of Computer Science Ensenada Mexico CINVESTAV-IPN Computer Science Department Mexico City Mexico

ISBN: (纸本)9798350308365

The Team Formation Problem in Social Networks (TFP-SN) describes the process of finding an effective group of people, drawn from a network of experts, to perform a particular task. For a team to be considered as effective, it requires to comply with a task-specific skills set while also showing a high degree of cohesiveness. Although team effectiveness is subject to multiple criteria, the study of the problem from a multi-objective (MO) perspective is still scarce. In this paper, we focus on an MO TFP-SN whose objective is to maximize the team's level of expertise and the team's density, simultaneously. To solve this problem, we introduce two novel recombination operators to be used within the framework of the well-known NSGA-II. Our proposed crossover operators act as heuristics that compute the parents' unique and shared information, which is then combined for generating potentially improved offspring. Our experiments show that each of the two proposed crossover operators lead to significantly better results when compared to a naive crossover operator taken from the specialized literature. Particularly, the results consistently show higher hypervolume values when compared to the use of an adaptation of a simple recombination operator commonly used for this problem. The good performance of our proposed operators may be attributed to the incorporation of knowledge that exploits the structure of the problem. © 2024 IEEE.

关键词： Combinatorial optimization Multi-objective optimization Recombination operators Team formation problem

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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Enhancing Scalability in Reinforcement Learning for Open Spaces

Enhancing Scalability in Reinforcement Learning for Open Spa...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Janjua, Jamshaid Iqbal Kousar, Shagufta Khan, Areeba Ihsan, Anaum Abbas, Tahir Saeed, Ali Q Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan Computer Center Northern Technical University Ninevah Iraq

ISBN: (纸本)9798350369106

Reinforcement Learning (RL) has been successful when the environment has specific objectives and boundaries. But with the emerging focus on open-world application which makes all or some of the rules or purpose go to naught, it makes traditional methods of RL a bit more difficult. This paper goes over various advancements and changes in Reinforcement Learning which can be employed for open-ended environments. Among the other strategies, hierarchical reinforcement learning, intrinsic motivation-based exploration, meta-learning and unsupervised skill acquisition are also among the ones that are examined. As a result, such a position based on the technology argues the promising future of open-ended methods for the management of complex problems and high level of uncertainty associated with the preset target or purpose. Also, we study cases in video games, robotics and autonomous systems, where RL is implemented in an open-ended and dynamic environment. We also outline existing limitations and perspectives, highlighting the need for more flexible methods and inter-scientific collaboration to fully realize RL's potential in open-ended contexts. © 2024 IEEE.

关键词： Adversarial machine learning

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From Monochrome to Technicolor: A Comprehensive Review of Video Colorization 25

From Monochrome to Technicolor: A Comprehensive Review of Vi...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Aydam, Zahoor M. El Abbadi, Nidhal K. University of Kufa Faculty of Computer Science and Mathematics Computer Science Department Najaf Iraq Al-Mustaqbal University Al-Mustaqbal Center for Ai Research and Applications Babylon Iraq

ISBN: (纸本)9798331540012

Video colorization is the process of adding colors to black and white video. This task is a compelling problem and intriguing challenge in image processing. With the emergence of deep learning and the availability of a wide range of datasets, significant developments have been made in video colorization algorithms. However, this task presents unique challenges, particularly in ensuring temporal coherence and maintaining spatiotemporal consistency across frames. In this work, we aim to offer a comprehensive overview of state-of-the-art techniques in video colorization, examining the challenges, progress in current techniques, and future direction, by analyzing many papers and research. The goal of this paper is to provide the researchers with a piece of valuable information about video colorization. © 2024 IEEE.

关键词： Video analysis

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IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization 38

IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ma, Xiaochen Zhu, Xuekang Su, Lei Du, Bo Jiang, Zhuohang Tong, Bingkui Lei, Zeyu Yang, Xinyu Pun, Chi-Man Lv, Jiancheng Zhou, Jizhe College of Computer Science Sichuan University China Department of Computer and Information Science University of Macao China Engineering Research Center of Machine Learning and Industry Intelligence MOE China

A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark;and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://***/scu-zjz/IMDLBenCo. © 2024 Neural information processing systems foundation. All rights reserved.

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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An ultra efficient 2:1 multiplexer using bar-shaped pattern in atomic silicon dangling bond technology

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Journal of Supercomputing 2024年第13期80卷 18347-18364页

作者： Rasmi, Hadi Mosleh, Mohammad Jafari Navimipour, Nima Kheyrandish, Mohammad Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

As CMOS technology approaches its physical and technical limits, alternative technologies such as nanotechnology or quantum computing are needed to overcome the challenges of lithography, transistor scaling, interconnects, and miniaturization. This article introduces a novel nanotechnology that uses atomic-scale silicon dangling bonds (ASDB) to create high-performance, low-power, nanoscale logic circuits. DBs are atoms that can form basic logic gates on a silicon surface using a scanning tunneling microscope device. ASDB can also be an alternative to the existing complementary metal oxide semiconductor (CMOS) technology. The article also proposes a new bar-shaped pattern to design gates and logic circuits with ASDB nano tecnolgoy. The bar-shaped pattern improves the reliability of the output, reduces the area and power consumption, and solves the problem of interatomic energy effects of ASDB. The article demonstrates the efficiency of the bar-shaped pattern by implementing two-input gates such as AND, NAND, OR, NOR, XOR, XNOR, and a 2:1 multiplexer with ASDB. The article also uses a powerful tool called SiQAD to simulate and verify the performance of the proposed structures with ASDB. According to the simulation results, the proposed logic gates are more energy efficient, stable, and compact than the previous structures. They consume 35% and 24.34% less energy and have 14.18% more stability, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Logic gates

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A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms

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Iranian Journal of Medical Physics 2024年第4期21卷 237-248页

作者： Firouzmand, Mohamad Majidzadeh, Keivan Jafari, Maryam Haghighat, Shahpar Esmaeili, Rezvan Moradi, Leila Misaghi, Nima Ensafi, Mahsa Batmanghelich, Fatemeh Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Biomedical engineering Department Iranian Research Organization for Science & Technology Tehran Iran Breast Cancer Research Center Motamed Cancer Institute ACECR Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Introduction: Several types of cancer can be detected early through thermography, which uses thermal profiles to image tissues in recent years, thermography has gained increasing attention due to its non-invasive and radiation-free nature. There is a growing need for thermographic images of breast cancer lesions in different nationalities and ages to develop this technique, however. This study aims to introduce a dataset of breast thermograms. Material and Methods: In this study, thermographic images of breast cancer from Iranian samples were prepared and confirmed due to the limited number of breast thermogram databases. The prepared database was tested using artificial intelligence and another well-known DMR database (Database for Mastology research) in this study to determine its reliability. Results: A variety of deep learning architectures and transfer learning are used to evaluate these databases for accuracy, sensitivity, speed, training compliance, and validation compliance. According to best-fitted structures for both types of databases, the database obtained from this study has a quality comparable to the DMR reference database, with minimum accuracy, sensitivity, specificity, precision, and F-score of 80%, 86%, 86%, 88%, and 87%, respectively. Conclusion: Using thermography as a method of early breast screening is demonstrated to be effective. In comparison to DMR, the lower statistics of the proposed database (between 2 and 7 percent) indicates that more diverse breast thermograms should be captured in conjunction with improvements to imaging equipment as well as adherence to thermography recording protocols in order to improve the reliability and efficiency of the database. © (2024), (Mashhad University of Medical sciences). All rights reserved.

关键词： Medical imaging

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