检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Marsja, Erik Thellman, Sam Anund, Anna Disability Research Division Department of Behavioural Sciences and Learning Linköping University Sweden Division for Human-Centered Systems Department of Computer and Information Science Linköping University Sweden The Swedish National Road Transport Research Institute Sweden

Automated shuttle buses (ASBs) are a promising technology for enhancing public transportation, but their acceptance and adoption depend on various factors, such as trust, expectations, and age. This study examined how trust in the capabilities of ASBs influences the behavioral intention to use ASBs as well as expectations on the braking behavior of ASBs, especially from the perspective of vulnerable road users (VRUs), such as pedestrians and cyclists. The study also investigated how age moderates these relationships. Five hundred participants completed an online questionnaire measuring their trust and behavioral intention to use ASBs. They then watched video scenarios of ASBs interacting with VRUs and reported their expectations regarding the ASBs’ ability to brake to avoid collisions. The data were analyzed using structural equation modeling (SEM). The results showed that trust was positively related to both behavioral intentions to use and expectations of ASBs’ braking behavior, and that age was negatively related to trust. However, age was not related to behavioral intention to use or expectations of ASBs’ braking behavior. The findings suggest that trust is a key factor influencing individuals’ acceptance and expectations of ASBs, and that trust can be enhanced by providing reliable, transparent, and user-friendly information and services. Furthermore, age is not a barrier to the adoption of ASBs, as long as individuals trust them. It offers practical implications for ASB developers, providers, and policymakers. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images 25th

MCCNet: Multi-Color Cascade Network with Weight Transfer for...

引用

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Advanced Web Traffic Modelling and Forecasting with a Hybrid Predictive Approach

引用

Journal of Web Engineering 2025年第3期24卷 409-456页

作者： Ujjwal Thakur Sunil K. Singh Sudhakar Kumar Harmanjot Singh Varsha Arya Brij B. Gupta Razaz Waheeb Attar Ahmed Alhomoud Kwok Tai Chui Department of CSE Chandigarh College of Engineering & Technology Chandigarh India Hong Kong Metropolitan University (HKMU) Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science and Information Engineering Asia University Taichung Taiwan China Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India University of Economics and Human Science Warsaw Poland Management Department College of Business Administration Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia

Web traffic analysis is crucial for optimising user experience and engagement. This research explores a hybrid approach combining traditional statistical methods, like the autoregressive integrated moving average (ARIMA) model, with advanced techniques such as long short-term memory (LSTM) neural networks and the Prophet model. ARIMA effectively captures linear trends, seasonal effects, and cyclic behaviours, while LSTM handles complex non-linear patterns, and Prophet addresses seasonal variations and missing data. The hybrid model demonstrated 93% accuracy in predicting web traffic, highlighting the benefits of integrating these methodologies. This approach enables businesses to better manage resources, boost user engagement, and improve revenue. Future research will focus on refining hybrid models by incorporating new data features and ensemble methods to further enhance prediction accuracy, ultimately advancing the understanding of web traffic trends and user behaviour.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms

引用

American Journal of human Genetics 2025年

作者： Zabad, Shadi Haryan, Chirayu Anant Gravel, Simon Misra, Sanchit Li, Yue School of Computer Science McGill University Montreal QC Canada Parallel Computing Lab Intel Labs Karnataka Bangalore India Department of Human Genetics McGill University Montreal QC Canada

With improved whole-genome sequencing and variant imputation techniques, modern genome-wide association studies (GWASs) have enriched our understanding of the landscape of genetic associations for thousands of disease phenotypes. However, translating the marginal associations for millions of genetic variants to integrated polygenic risk scores (PRSs) that capture their joint effects on the phenotype remains a major challenge. Due to technical and statistical constraints, commonly used PRS methods in this setting either perform heuristic pruning and thresholding or overlook most genetic association signals by restricting inference to small variant sets, such as HapMap3. Here, we present a set of algorithmic improvements and compact data structures that enable scaling summary-statistics-based PRS inference to tens of millions of variants while avoiding numerical instabilities common in such high-dimensional settings. These enhancements consist of highly compressed linkage-disequilibrium (LD) matrix format, which integrates with streamlined and parallel coordinate-ascent updating schemes. When incorporated into our existing PRS method (VIPRS), the proposed algorithms yield over 50-fold reductions in storage requirements and lead to orders-of-magnitude improvements in runtime and memory efficiency. The updated VIPRS software can now perform variational Bayesian regression over 1.1 million HapMap3 variants in under a minute. Using this scalable implementation, we applied VIPRS to 75 of the most heritable, continuous phenotypes in the UK Biobank, leveraging marginal associations for up to 18 million bi-allelic variants. These experiments demonstrated that VIPRS is 1–2 orders of magnitude more efficient than popular baselines while being competitive with the best-performing methods in terms of prediction accuracy. © 2025 The Authors

关键词： algorithms Bayesian inference GWAS inference LD linkage disequilibrium polygenic prediction polygenic risk score whole-genome analysis

来源：评论

学校读者我要写书评

暂无评论

Scalable Machine Learning Frameworks for Large-Scale Multimodal Image and Speech Signal Processing 8

Scalable Machine Learning Frameworks for Large-Scale Multimo...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Dankan Gowda, V. Pathak, Disha Prasad, K.D.V. Srinivas, Ved Manu, Y.M. Reddy, N. Sudhakar Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Centre for Management Studies-Hyderabad Mulshi Pune Maharashtra Lavale412115 India Symbiosis Institute of Business Management Hyderabad Pune India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India Adichunchanagiri University Bgs Institute of Technology Department of Computer Science and Engineering Nagamangala Taluk B.G.Nagara Karnataka Mandya District India Mohan Babu University School of Engineering Department of Ece Tirupati India

ISBN: (纸本)9798350376425

The machine learning algorithm proposed in this paper is suitable for Big Data multimodal datasets and in particular for integrating image and speech data. Preliminary feature extraction is based on convolutional neural network (CNN) for image analysis and recurrent neural network (RNN) for speech processing, while the fusion was done in a fusion layer. For assessment, the system's performance is compared on the aspect of accuracy and latency, computational overhead, and resource consumption. The above experiment results prove that the proposed framework reduces the latency to 120ms and the required computational power, but the training and validation set accuracies as high as 90% and 87% are higher than the corresponding values of basic models such as CNN and hybrids of CNN+LSTM. It also illustrates the system's scalability: inference time of less than 2s in the range of moderate batch size, and lower memory consumption (110 MB) in comparison with the baseline models. The characteristics of the model mean that it takes less resources to process data, making the model ideal for use in applications where there is a need for processing of data in real time. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading 31

Self-supervised learning of a tailored Convolutional Auto En...

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Tabatabaei, Zahra Colomer, Adrián Engan, Kjersti Oliver, Javier Naranjo, Valery Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano Spain HUMAN-tech Universitat Politècnica de València Spain ValgrAI - Valencian Graduate School Research Network for Artificial Intelligence Spain Department of Electrical Engineering and Computer Science University of Stavanger Norway Department of Artificial Intelligence Tyris Tech S.L. Valencia Spain

ISBN: (纸本)9789464593600

According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostate histopathological images when labeled images are scarce. In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128×128×3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task. The downstream task of the proposed SSL paradigm is the automatic grading of histopathological patches of prostate cancer. The presented framework reports promising results on the validation set, obtaining an overall accuracy of 83% and on the test set, achieving an overall accuracy value of 76% with F1-score of 77% in G4. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

Incremental Feature Selection for Dynamic Incomplete Data Using Sub-Tolerance Relations

SSRN

引用

SSRN 2023年

作者： Zhao, Jie Ling, Yun See-To, Wing-Kuen Eric Huang, Faliang Wang, Jiahai School of Management Guangdong University of Technology Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Feature selection is an important problem in Rough Set and is widely used in data analysis. The extended theory of tolerance relations can effectively deal with the realistic problem of incomplete data. However, the space and time complexity of computing the positive region using TRS increases dramatically. Simultaneously, redundant features are allowed to enter the feature subset, resulting in poor classification accuracy, because the positive region-based significance measure neglects uncertain information. To overcome these drawbacks, this paper introduces a subrelation-based tolerance class (STC) approach. STC transforms the TRS positive region calculation into a two-stage certainty measurement of objects, and thus divides the entire universe into two regions of certainty and uncertainty at each stage, by decomposing the tolerance relation into two subrelations. The computational space is reduced by progressively filtering out of the regions of certainty. Using STC, a new incremental mechanism is proposed. Furthermore, we propose a new significance measure using both certain and uncertain information, which effectively reduces the number of redundant features and subsequently improves computational efficiency and classification accuracy. Experiments on real data sets and comparisons with existing algorithms have been conducted to verify the superiority of the method. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification

Domain-Specific Pre-training Improves Confidence in Whole Sl...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Soham Rohit Chitnis Sidong Liu Tirtharaj Dash Tanmay Tulsidas Verlekar Antonio Di Ieva Shlomo Berkovsky Lovekesh Vig Ashwin Srinivasan Department of Computer Science & Information Systems Birla Institute of Technology and Science Pilani Faculty of Medicine Health and Human Sciences Macquarie University Boolean Lab University of California San Diego TCS Research India

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models’ confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://***/soham-chitnis10/WSI-domain-specific.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

arXiv

引用

arXiv 2023年

作者： Marinov, Zdravko Jäger, Paul F. Egger, Jan Kleesiek, Jens Stiefelhagen, Rainer The Computer Vision for Human-Computer Interaction Lab Department of Informatics Karlsruhe Institute of Technology Adenauerring 10 Karlsruhe76131 Germany Girardetstraße 2 Essen45131 Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 223 Heidelberg69120 Germany The Helmholtz Imaging DKFZ Im Neuenheimer Feld 223 Heidelberg69120 Germany

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Appendixes are here. © 2023, CC BY.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：