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Collaborative Framework for circRNA-Disease Associations Prediction using Dual Variational Graph

IEEE Transactions on Big Data 2025年

作者： Liu, Changchun Wang, Lei Zhao, Bowei Wei, Mengmeng Li, Yang Lu, Mianshuo Wei, Yu Liang, Sizhe The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Key Lab of Human-machine Interaction and Intelligent Decision Guangxi Acad emy of Science Nanning530007 China The School of Computer Science and Information Engineer ing Hefei University of Technology Hefei230601 China The College of Computer and Information Science School of Software University of Chinese Academy of Sciences Urumqi830011 China

Many experiments have shown that circular RNA (circRNA) can act as biomarkers for complex diseases and play significant regulatory roles in multiple pathological processes. However, most circRNA-disease associations remain unknown, and discovering these associations through biological experimental approach is expensive and time-consuming. Taking into account the shortcomings of current methods, we introduce a new collaborative framework that utilizes multi-heterogeneous graphs, along with variational graph auto-encoders (VGAE) to predict associations between circRNA and diseases. First, we build multi-similarity networks using various biological attributes of circRNA and diseases, and integrate these similarity networks. Two subnetworks are constructed from association matrix and combined similarity network, which included a circRNA-based network and a disease-based network. We then employ random walk with restart and Singular Value Decomposition, for feature extraction from the similarity matrix. Finally, we use collaborative framework to predict the circRNA-disease association scores based on the two subnetworks. We integrate the two score matrices to obtain a final prediction scoring matrix. Using 5-fold cross-validation on the CircR2Disease dataset, our model achieved an AUC score of 0.9828 and an AUPR score of 0.9820. Additionally, among the top 30 highest-scoring circRNA-disease association pairs, 26 associations have already been validated. Our model shows strong performance and can accurately predict associations between circRNA and diseases, according to experimental results. © 2015 IEEE. All rights reserved.

关键词： Variational techniques

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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A game-based approach for evaluating and customizing handwriting training using an autonomous social robot

A game-based approach for evaluating and customizing handwri...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Daniel Carnieto Tozadore Chenyang Wang Giorgia Marchesi Barbara Bruno Pierre Dillenbourg Computer-Human Interaction in Learning and Instruction (CHILI) Lab EPFL Lausanne Switzerland MOBOTS Group EPFL Lausanne Switzerland Dept. DIBRIS University of Genova Italy

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Handwriting learning is a long and complex process that takes about ten years to be fully mastered. Nearly one-third of all children aged 4-12 experiences handwriting difficulties and, sadly, most of them are left to fight them on their own, due to the scarcity of tools for the detection and remediation of such difficulties. Building on state-of-the-art digital solutions for automated handwriting assessment and the training of specific handwriting-related skills, in this article we discuss requirements, rationale, and architecture of a system for handwriting training, which relies on a social robot as a mediator agent, offering personalized training and suggestions. The system is envisioned to operate autonomously and to support long-term interactions via personalization. Preliminary validation of the system in an experiment with 31 children showed its potential not only for autonomously guiding handwriting training sessions, but also for its inclusion in the teachers’ practice.

关键词： Training Architecture Social robots Buildings Systems architecture computer architecture Aging

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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration 25

Letters from Future Self: Augmenting the Letter-Exchange Exe...

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

作者： Hayeon Jeon Suhwoo Yoon Keyeun Lee Seo Hyeong Kim Esther Hehsun Kim Seonghye Cho Yena Ko Soeun Yang Laura Dabbish John Zimmerman Eun-mee Kim Hajin Lim hci+d lab. Seoul National University Seoul Republic of Korea Department of Communication Seoul National University Seoul Republic of Korea Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA HCI Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

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Video Face Clustering with Self-Supervised Representation Learning

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 145-157页

作者： Sharma, Vivek Tapaswi, Makarand Saquib Sarfraz, M. Stiefelhagen, Rainer Department of Informatics Institute for Anthropomatics and Robotics Computer Vision for Human-Computer Interaction Lab Karlsruhe Institute of Technology Karlsruhe76131 Germany University of Toronto TorontoONM5S 3G4 Canada Massachusetts Institute of Technology CambridgeMA02139 United States Vector Institute TorontoON Canada Inria Paris75012 France

Characters are a key component of understanding the story conveyed in TV series and movies. With the rise of advanced deep face models, identifying face images may seem like a solved problem. However, as face detectors get better, clustering and identification need to be revisited to address increasing diversity in facial appearance. In this paper, we propose unsupervised methods for feature refinement with application to video face clustering. Our emphasis is on distilling the essential information, identity, from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, that can also be applied to image collections. We evaluate our methods on three video face clustering datasets. Thorough experiments including generalization studies show that our methods outperform current state-of-the-art methods on all datasets. The datasets and code are available at https://***/vivoutlaw/SSIAM. © 2019 IEEE.

关键词： computer vision

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FastForce: Real-Time Reinforcement of Laser-Cut Structures 21

FastForce: Real-Time Reinforcement of Laser-Cut Structures

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Proceedings of the 2021 CHI Conference on human Factors in Computing Systems

作者： Muhammad Abdullah Martin Taraz Yannis Kommana Shohei Katakura Robert Kovacs Jotaro Shigeyama Thijs Roumen Patrick Baudisch Hasso Plattner Institute Germany Human Computer Interaction Lab Hasso Plattner Institute Germany

ISBN: (纸本)9781450380966

We present fastForce, a software tool that detects structural flaws in laser cut 3D models and fixes them by introducing additional plates into the model, thereby making models up to 52x stronger. By focusing on a specific type of structural issue, i.e., poorly connected sub-structures in closed box structures, fastForce achieves real-time performance (106x faster than finite element analysis, in the specific case of the wheelbarrow from Figure 1). This allows fastForce to fix structural issues continuously in the background, while users stay focused on editing their models and without ever becoming aware of any structural *** our study, six of seven participants inadvertently introduced severe structural flaws into the guitar stands they designed. Similarly, we found 286 of 402 relevant models in the kyub [1] model library to contain such flaws. We integrated fastForce into a 3D editor for lasercutting (kyub) and found that even with high plate counts fastForce achieves real-time performance.

关键词： Personal fabrication structural analysis lasercutting structural reinforcement

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Towards Understanding Interactive Sonic Gastronomy with Chefs and Diners 25

Towards Understanding Interactive Sonic Gastronomy with Chef...

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

作者： Hongyue Wang Jialin Deng Linjia He Nathalie Overdevest Ryan Wee Yan Wang Phoebe O. Toups Dugas Don Samitha Elvitigala Florian Floyd Mueller Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Diverse-ability Interaction Lab School of Computer Science Bristol United Kingdom Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Mineral Resources Commonwealth Scientific and Industrial Research Organisation Melbourne Australia

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Overscan Detection in Digitized Analog Films by Precise Sprocket Hole Segmentation 15th

Overscan Detection in Digitized Analog Films by Precise Spro...

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15th International Symposium on Visual Computing, ISVC 2020

作者： Helm, Daniel Kampel, Martin Institute of Visual Computing and Human-Centered Technology Computer Vision Lab TU Wien Vienna Austria

ISBN: (纸本)9783030645557

Automatic video analysis is explored in order to understand and interpret real-world scenes automatically. For digitized historical analog films, this process is influenced by the video quality, video composition or scan artifacts called overscanning. The main aim of this paper is to find the Sprocket Holes (SH) in digitized analog film frames in order to drop unwanted overscan areas and extract the correct scaled final frame content which includes the most significant frame information. The outcome of this investigation proposes a precise overscan detection pipeline which combines the advantages of supervised segmentation networks such as DeeplabV3 with an unsupervised Gaussian Mixture Model for fine-grained segmentation based on histogram features. Furthermore, this exploration demonstrates the strength of using low-level backbone features in combination with low-cost CNN architectures like SqueezeNet in terms of inference runtime and segmentation performance. Moreover, a pipeline for creating photo-realistic frame samples to build a self-generated dataset is introduced and used in the training and validation phase. This dataset consists of 15000 image-mask pairs including synthetically created and deformed SHs with respect to the exact film reel layout geometry. Finally, the approach is evaluated by using real-world historical film frames including original SHs and deformations such as scratches, cracks or wet splices. The proposed approach reaches a Mean Intersection over Union (mIoU) score of 0.9509 (@threshold: 0.5) as well as a Dice Coefficient of 0.974 (@threshold: 0.5) and outperforms state-of-the-art solutions. Finally, we provide full access to our source code as well as the self-generated dataset in order to promote further research on digitized analog film analysis and fine-grained object segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Sprockets

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Immersive Virtual Reality Improves Psychophysiological Responses to Agent-Guided Breathing Training

SSRN

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SSRN 2025年

作者： Corrò, Christian Chittaro, Luca Human-Computer Interaction Lab Department of Mathematics Computer Science and Physics University of Udine Via delle Scienze 206 Udine33100 Italy

Slow deep breathing (SDB) techniques offer physical and mental health benefits that help mitigate stress, anxiety, and pain. However, economic and logistical barriers limit access to traditional breathing training programs. While self-help resources like videos and apps are available, they lack the personalized guidance and feedback provided by human trainers and tend to have high dropout rates. The increasing availability of consumer-grade virtual reality (VR) headsets presents new opportunities for immersive breathing training experiences, potentially enhancing engagement and adherence. However, it remains unclear whether breathing training can benefit from immersive VR (iVR). This paper addresses this question by comparing agent-guided breathing training in iVR (headset-based) vs. non-immersive VR (based on a computer monitor). We introduce and evaluate a virtual agent that provides users with real-time, personalized feedback on their respiratory frequency and depth. We evaluate both self-reported measures (including engagement, attention, presence, and perception of the virtual agent) and objective physiological signals, comprising respiration, electrodermal activity, cardiac activity, and brain prefrontal cortex activity (acquired through functional near-infrared spectroscopy, fNIRS). The results of the user study on 36 participants highlighted several benefits of iVR over non-immersive VR. Results showed that iVR increased users’ attention, engagement, sense of presence, social presence, and perceived anthropomorphism of the virtual agent. Physiologically, iVR improved depth of breathing at the correct SDB frequency, reduced cognitive load, decreased sympathetic arousal and increased parasympathetic activity. The paper describes and discusses these findings, highlighting the important role that iVR can play in enhancing the effectiveness of breathing training. © 2025, The Authors. All rights reserved.

关键词： Audition

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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