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arXiv 2023年

作者： Ding, Jian Xue, Nan Xia, Gui-Song Schiele, Bernt Dai, Dengxin NERCMS School of Computer Science Wuhan University China State Key Lab. LIESMARS Wuhan University China Max Planck Institute for Informatics Saarland Informatics Campus Germany

Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. This work studies semantic segmentation under the domain generalization setting, where a model is trained only on the source domain and tested on the unseen target domain. Existing works show that Vision Transformers are more robust than CNNs and show that this is related to the visual grouping property of self-attention. In this work, we propose a novel hierarchical grouping transformer (HGFormer) to explicitly group pixels to form part-level masks and then whole-level masks. The masks at different scales aim to segment out both parts and a whole of classes. HGFormer combines mask classification results at both scales for class label prediction. We assemble multiple interesting cross-domain settings by using seven public semantic segmentation datasets. Experiments show that HGFormer yields more robust semantic segmentation results than per-pixel classification methods and flat-grouping transformers, and outperforms previous methods significantly. Code will be available at https://***/dingjiansw101/HGFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection

arXiv

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arXiv 2024年

作者： Cao, Liujuan Lin, Jianghang Hong, Zebo Shen, Yunhang Lin, Shaohui Chen, Chao Ji, Rongrong Media Analytics and Computing Lab School of Informatics Xiamen University 361102 China Youtu Lab Tencent200233 China School of Computer Science and Technology East China Normal University 200062 China

As an emerging problem in computer vision, weakly supervised object detection (WSOD) aims to use only image-level annotations to train object detectors. Most WSOD methods rely on traditional object proposals to generate candidate regions and are confronted with unstable training, which easily gets stuck in a poor local optimum. In this paper, we propose a unified and high-capacity WSOD network with holistic self-training framework, termed HUWSOD, which is self-contained, and requires no external modules or additional supervision. To this end, the proposed framework innovates in two perspectives, i.e., unified network structure and self-training scheme, both of which are rarely touched in WSOD before. First, we design a self-supervised proposal generator and an autoencoder proposal generator with multi-rate resampling pyramid to hypothesize object locations, which replace the traditional object proposals and enable the strictly end-to-end training and inference for WSOD. Second, we introduce holistic self-training scheme that consists of step-wise entropy minimization and consistency-constraint regularization, which refine both detection scores and coordinates progressively, as well as enforcing consistency to match predictions produced from stochastic augmentations of the same image. Extensive experiments on PASCAL VOC and MS COCO show that the proposed HUWSOD performs competitively to the state-of-the-art WSOD methods, while getting rid of offline proposals and additional data. Moreover, the upper-bound performance of HUWSOD with class-agnostic ground-truth bounding boxes approaches Faster R-CNN, which demonstrates that HUWSOD is capable to achieve fully-supervised accuracy. Our work also brings one important finding that random initialized boxes, although drastically different from the offline well-designed object proposals, are also effective object candidates for WSOD training. The code is available at: https://***/shenyunhang/HUWSOD. Copyright © 2024, Th

关键词： Object detection

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Equivalence of Product Ciphers to Substitution Ciphers and their Security Implications

Equivalence of Product Ciphers to Substitution Ciphers and t...

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2022 International Symposium on Networks, computers and Communications, ISNCC 2022

作者： Carlson, Albert Mikkilineni, Sai Ranganath Totaro, Michael W. Wells, Richard B. Hiromoto, Robert E. Austin Community College Computer Science Department AustinTX United States Research Lab School of Computing and Informatics LafayetteLA United States Microelectronics Research and Communications Institute University of Idaho MoscowID United States University of Idaho Department of Computer Science MoscowID United States

ISBN: (数字)9781665485449

ISBN: (纸本)9781665485449

Product/Compound ciphers composed of multi-byte permutations and substitutions are generally considered to be more secure than their standalone individual component-cipher counterparts (i.e., substitution (S) and permutation (P) ciphers). In this paper, we show that a permutation-substitution-permutation (PSP) cipher that uses any form of regular byte-block boundaries, along with a regular encoding (such as ASCII), is no more secure than a multi-byte S cipher. In addition, we also show that, under certain conditions, a PSP cipher can be reduced to an S cipher. In addition to introducing the concept of isomorphic cipher reduction, we show that our theoretical findings translate into practical means by using a plaintext attack. In doing so, we shed light on the problems of encryption security that arise due to employing a product cipher in conjunction with any form of regular byte-block boundaries, and propose appropriate countermeasures. © 2022 IEEE.

关键词： Cryptography

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Beyond the Watercooler: Designing for computer-Mediated Self-Disclosure among Work Colleagues 25

Beyond the Watercooler: Designing for Computer-Mediated Self...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Kevin Chow Joanna McGrenere Thomas Fritz Lucas L Puente Michael Massimi Department of Computer Science University of British Columbia Vancouver British Columbia Canada Department of Informatics University of Zurich Zurich Switzerland Workforce Lab Slack San Francisco California USA

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Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf 38

Learning to Discuss Strategically: A Case Study on One Night...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Xuanfa Wang, Ziyan Du, Yali Fang, Meng Zhang, Haifeng Wang, Jun Institute of Automation Chinese Academy of Sciences China Cooperative AI Lab Department of Informatics King's College London United Kingdom School of Artificial Intelligence University of Chinese Academy of Sciences China University of Liverpool United Kingdom Nanjing Artificial Intelligence Research of IA China AI Centre Department of Computer Science UCL United Kingdom

Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games. As a variant of the famous communication game Werewolf, One Night Ultimate Werewolf (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game. In this work, we first present the existence of the Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with discussion and one without. The results showcase that the discussion greatly changes players' utilities by affecting their beliefs, emphasizing the significance of discussion tactics. Based on the insights obtained from the analyses, we propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt. Our experimental results on several ONUW game settings demonstrate the effectiveness and generalizability of our proposed framework. The project page of our paper: ***. © 2024 Neural information processing systems foundation. All rights reserved.

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A secured GA-WPC framework for scheduling the independent tasks in cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 13003-13015页

作者： Dubey, Kalka Sharma, S.C. Kumar, Mohit Kumar, Prashant Nasr, Aida A. Cloud Computing and Wireless Sensor Lab Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais Uttar Pradesh Amethi229304 India Department of Information Technology Dr BR Ambedkar National Institute of Technology Punjab Jalandhar144011 India Information Technology Department Faculty of Computers and Informatics Tanta University Tanta Egypt

Cloud computing proved itself a powerful and popular platform for providing computing services. It offers on-demand services to users with highly efficient resources over the internet. With the exponential increase in the number of internet users and due to the high acceptance rate to use the cloud services in everything, the demand for cloud platforms has also increased significantly. This leads to heavy workloads on the servers, which is a difficult task to manage due to the heterogeneity and uncertainty of the resources. However, several traditional, heuristic, and evolutionary-based approaches have been proposed to solve the problem of scheduling. Existing state-of-art techniques and frameworks have solved the problem up to a limited level. Still, these solutions have not given any effective and concrete solution for the overall resource scheduling problem. Therefore, this paper tries to overcome the bottleneck of existing solutions by presenting a new framework named Genetic Algorithm Water Pressure Change (GAWPC) to solve task scheduling problems efficiently. The GAWPC framework exploited the feature of the Genetic Algorithm (GA) and Water Pressure Change optimization (WPCO) techniques to form a new hybrid mechanism. In the proposed framework, GA has been deployed to find the optimal solution for a particular population. WPCO has been used to optimize fitness function for a fast convergence rate. The computer-based simulation has demonstrated promising benefits like improved computation cost, make-span time, and load balancing when compared with the most recent existing baseline techniques using CloudSim. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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A comparative study of machine learning models with LASSO and SHAP feature selection for breast cancer prediction

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Healthcare Analytics 2024年 6卷 100353-100353页

作者： Shaon, Md. Shazzad Hossain Karim, Tasmin Shakil, Md. Shahriar Hasan, Md. Zahid Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Dhaka Birulia 1216 Bangladesh

In recent decades, breast cancer has become the most prevalent type of cancer that impacts women in the world, which shows a significant risk to the death rates of women. Early identification of breast cancer might drastically decrease patient mortality and greatly improve the chance of an effective treatment. In modern times, machine learning models have become crucial for classifying cancer and strengthening both the accuracy and efficiency of diagnostic and medical treatment strategies. Therefore, this study is focused on early detection of breast cancer using a variety of machine learning algorithms and desires to identify the most effective feature selection process with an amalgamated dataset. Initially, we evaluated five traditional models and two meta-models on separate datasets. To find the most valuable features, the study used the Least Absolute Shrinkage and Selection Operator (LASSO) as well as SHapley Additive exPlanations (SHAP) selection methods and analyzed them through a wide range of performance regulations. Additionally, we applied these models to the combined dataset and observed that the mergeddataset was significantly beneficial for breast cancer diagnosis. After analyzing the feature selection strategies, it was demonstrated that the majority of models performed more accurately when utilizing SHAP methodologies. Notably, three traditional models and two meta-classifiers obtained an accuracy of 99.82%, demonstrating superior performance compared to state-of-the-art methods. This advancement holds a crucial role as it lays the foundation for refining diagnostic tools and enhancing the progression of medical science in this field. © 2024

关键词： Breast cancer Diagnostic tools Feature selection Machine learning Meta-models SHapley additive exPlanations (SHAP)

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Meta-Transfer Learning for Few-Shot Meningioma Segmentation

Meta-Transfer Learning for Few-Shot Meningioma Segmentation

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IEEE International Symposium on Biomedical Imaging

作者： Shenghui Yan Sidong Liu Antonio Di Ieva Maurice Pagnucco Yang Song School of Computer Science and Engineering University of New South Wales Australia Centre for Health Informatics Macquarie University Australia Computational NeuroSurgery Lab Macquarie University Sydney Australia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Many algorithms have been developed for brain tumor segmentation over the past years, especially since the inception of the BraTS challenge. However, these models mainly focus on glioma segmentation because of their relatively high incidence. Their performance may not hold for other types of brain tumors, such as meningioma, without a large number of samples to re-train or fine-tune the models. In this work, we propose a new meta-transfer learning network for few-shot meningioma segmentation that combines meta-learning and transfer learning. The proposed meta-transfer learning framework learns shared common knowledge using a large amount of data from more easily accessible glioma data, and then adapts quickly to meningiomas with few-shot cases. We show that our meta-transfer learning gains a respective 29.88% and 5.63% increase in Dice score over few-shot transfer learning and few-shot meta-learning, respectively; and achieves comparable performance against its fully-supervised counterpart while only requiring 2% of its training data.

关键词： Metalearning Adaptation models Solid modeling Image segmentation Three-dimensional displays Transfer learning Training data

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A Multimodal Approach for Fluid Overload Prediction: Integrating Lung Ultrasound and Clinical Data

arXiv

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arXiv 2024年

作者： Yang, Tianqi Anantrasirichai, Nantheera Karakuş, Oktay Allinovi, Marco Achim, Alin Visual Information Lab University of Bristol Bristol United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Careggi University Hospital Florence Italy

Managing fluid balance in dialysis patients is crucial, as improper management can lead to severe complications. In this paper, we propose a multimodal approach that integrates visual features from lung ultrasound images with clinical data to enhance the prediction of excess body fluid. Our framework employs independent encoders to extract features for each modality and combines them through a cross-domain attention mechanism to capture complementary information. By framing the prediction as a classification task, the model achieves significantly better performance than regression. The results demonstrate that multimodal models consistently outperform single-modality models, particularly when attention mechanisms prioritize tabular data. Pseudo-sample generation further contributes to mitigating the imbalanced classification problem, achieving the highest accuracy of 88.31%. This study underscores the effectiveness of multimodal learning for fluid overload management in dialysis patients, offering valuable insights for improved clinical outcomes. Copyright © 2024, The Authors. All rights reserved.

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Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model

arXiv

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arXiv 2023年

作者： Zhang, Xiang Gu, Yu Yan, Huan Wang, Yantong Dong, Mianxiong Ota, Kaoru Ren, Fuji Ji, Yusheng The School of Computer and Information Hefei University of Technology China I+ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China China The Dept. of Sciences and Informatics Muroran Institute of Technology Japan The National Institute of Informatics Tokyo101-8430 Japan

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras;however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs. © 2023, CC BY.

关键词： Wireless local area networks (WLAN)

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