ArXiv Pub Date : 2024-03-12 DOI: 10.1117/12.3008830

Yike Zhang, Eduardo Davalos, Dingjie Su, Ange Lou, Jack H. Noble

{"title":"Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery","authors":"Yike Zhang, Eduardo Davalos, Dingjie Su, Ange Lou, Jack H. Noble","doi":"10.1117/12.3008830","DOIUrl":"https://doi.org/10.1117/12.3008830","url":null,"abstract":"For those experiencing severe-to-profound sensorineural hearing loss, the cochlear implant (CI) is the preferred treatment. Augmented reality (AR) aided surgery can potentially improve CI procedures and hearing outcomes. Typically, AR solutions for image-guided surgery rely on optical tracking systems to register pre-operative planning information to the display so that hidden anatomy or other important information can be overlayed and co-registered with the view of the surgical scene. In this paper, our goal is to develop a method that permits direct 2D-to-3D registration of the microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our proposed solution involves using surface mapping of a portion of the incus in surgical recordings and determining the pose of this structure relative to the surgical microscope by performing pose estimation via the perspective-n-point (PnP) algorithm. This registration can then be applied to pre-operative segmentations of other anatomy-of-interest, as well as the planned electrode insertion trajectory to co-register this information for the AR display. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively. Our proposed method has the potential to be applicable and generalized to other surgical procedures while only needing a monocular microscope during intra-operation.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140394480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models 分析对序列到序列相关性模型的对抗性攻击

ArXiv Pub Date : 2024-03-12 DOI: 10.1007/978-3-031-56060-6_19

Andrew Parry, Maik Fröbe, Sean MacAvaney, Martin Potthast, Matthias Hagen

引用次数: 0

Perennial Semantic Data Terms of Use for Decentralized Web 去中心化网络的常年语义数据使用条款

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3589334.3645631

Rui Zhao, Jun Zhao

{"title":"Perennial Semantic Data Terms of Use for Decentralized Web","authors":"Rui Zhao, Jun Zhao","doi":"10.1145/3589334.3645631","DOIUrl":"https://doi.org/10.1145/3589334.3645631","url":null,"abstract":"In today's digital landscape, the Web has become increasingly centralized, raising concerns about user privacy violations. Decentralized Web architectures, such as Solid, offer a promising solution by empowering users with better control over their data in their personal `Pods'. However, a significant challenge remains: users must navigate numerous applications to decide which application can be trusted with access to their data Pods. This often involves reading lengthy and complex Terms of Use agreements, a process that users often find daunting or simply ignore. This compromises user autonomy and impedes detection of data misuse. We propose a novel formal description of Data Terms of Use (DToU), along with a DToU reasoner. Users and applications specify their own parts of the DToU policy with local knowledge, covering permissions, requirements, prohibitions and obligations. Automated reasoning verifies compliance, and also derives policies for output data. This constitutes a ``perennial'' DToU language, where the policy authoring only occurs once, and we can conduct ongoing automated checks across users, applications and activity cycles. Our solution is built on Turtle, Notation 3 and RDF Surfaces, for the language and the reasoning engine. It ensures seamless integration with other semantic tools for enhanced interoperability. We have successfully integrated this language into the Solid framework, and conducted performance benchmark. We believe this work demonstrates a practicality of a perennial DToU language and the potential of a paradigm shift to how users interact with data and applications in a decentralized Web, offering both improved privacy and usability.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast-Forward Reality: Authoring Error-Free Context-Aware Policies with Real-Time Unit Tests in Extended Reality 快进现实：在扩展现实中通过实时单元测试编写无差错的上下文感知策略

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3613904.3642158

Xun Qian, Tianyi Wang, Xuhai Xu, Tanya R. Jonker, Kashyap Todi

引用次数: 0

Proactive Recommendation with Iterative Preference Guidance 通过迭代偏好指导进行主动推荐

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3589335.3651548

Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng, Xiangnan He

{"title":"Proactive Recommendation with Iterative Preference Guidance","authors":"Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng, Xiangnan He","doi":"10.1145/3589335.3651548","DOIUrl":"https://doi.org/10.1145/3589335.3651548","url":null,"abstract":"Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization. To counteract this, proactive recommendation actively steers users towards developing new interests in a target item or topic by strategically modulating recommendation sequences. Existing work for proactive recommendation faces significant hurdles: 1) overlooking the user feedback in the guidance process; 2) lacking explicit modeling of the guiding objective; and 3) insufficient flexibility for integration into existing industrial recommender systems. To address these issues, we introduce an Iterative Preference Guidance (IPG) framework. IPG performs proactive recommendation in a flexible post-processing manner by ranking items according to their IPG scores that consider both interaction probability and guiding value. These scores are explicitly estimated with iteratively updated user representation that considers the most recent user interactions. Extensive experiments validate that IPG can effectively guide user interests toward target interests with a reasonable trade-off in recommender accuracy. The code is available at https://github.com/GabyUSTC/IPG-Rec.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation 单源领域泛化的不确定性引导对比学习

ArXiv Pub Date : 2024-03-12 DOI: 10.1109/icassp48485.2024.10448096

Anastasios Arsenos, D. Kollias, Evangelos Petrongonas, Christos Skliros, S. Kollias

引用次数: 0

From Files to Streams: Revisiting Web History and Exploring Potentials for Future Prospects 从文件到信息流：重温网络历史，探索未来发展潜力

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3589335.3652001

Lucas Vogel, Thomas Springer, Matthias Wählisch

引用次数: 0

Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation 通过域偏移缓解基于 GAN 的图像翻译中的模型提取攻击

ArXiv Pub Date : 2024-03-12 DOI: 10.1609/aaai.v38i18.29966

Di Mi, Yanjun Zhang, Leo Yu Zhang, Shengshan Hu, Qi Zhong, Haizhuan Yuan, Shirui Pan

{"title":"Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation","authors":"Di Mi, Yanjun Zhang, Leo Yu Zhang, Shengshan Hu, Qi Zhong, Haizhuan Yuan, Shirui Pan","doi":"10.1609/aaai.v38i18.29966","DOIUrl":"https://doi.org/10.1609/aaai.v38i18.29966","url":null,"abstract":"Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim deep neural network (DNN) model by only querying its API service remotely, posing a severe threat to the security and integrity of pay-per-query DNN-based services. Although the majority of current research on MEAs has primarily concentrated on neural classifiers, there is a growing prevalence of image-to-image translation (I2IT) tasks in our everyday activities. However, techniques developed for MEA of DNN classifiers cannot be directly transferred to the case of I2IT, rendering the vulnerability of I2IT models to MEA attacks often underestimated. This paper unveils the threat of MEA in I2IT tasks from a new perspective. Diverging from the traditional approach of bridging the distribution gap between attacker queries and victim training samples, we opt to mitigate the effect caused by the different distributions, known as the domain shift. This is achieved by introducing a new regularization term that penalizes high-frequency noise, and seeking a flatter minimum to avoid overfitting to the shifted distribution. Extensive experiments on different image translation tasks, including image super-resolution and style transfer, are performed on different backbone victim models, and the new design consistently outperforms the baseline by a large margin across all metrics. A few real-life I2IT APIs are also verified to be extremely vulnerable to our attack, emphasizing the need for enhanced defenses and potentially revised API publishing policies.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Assisted Causal Pathway Diagram for Human-Centered Design 人工智能辅助因果路径图，实现以人为本的设计

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3613904.3642179

Ruican Zhong, Donghoon Shin, Rosemary Meza, P. Klasnja, Lucas Colusso, Gary Hsieh

引用次数: 0

From Paper to Card: Transforming Design Implications with Generative AI 从纸张到卡片：用生成式人工智能改变设计含义

ArXiv Pub Date : 2024-03-12 DOI: 10.1145/3613904.3642266

Donghoon Shin, Lucy Lu Wang, Gary Hsieh

引用次数: 0

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