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arXiv 2025年

作者： Liang, Xiucheng Xie, Jinheng Zhao, Tianhong Stouffs, Rudi Biljecki, Filip Department of Architecture National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore College of Big Data and Internet Shenzhen Technology University Shenzhen China Department of Real Estate National University of Singapore Singapore

Building properties, such as height, usage, and material composition, play a crucial role in spatial data infrastructures, supporting applications such as energy simulation, risk assessment, and environmental modeling. Despite their importance, comprehensive and high-quality building attribute data remain scarce in many urban areas. Recent advances have enabled the extraction and tagging of objective building attributes using remote sensing and street-level imagery. However, establishing a method and pipeline that integrates diverse open datasets, acquires holistic building imagery at scale, and infers comprehensive building attributes remains a significant challenge. Among the first, this study bridges the gaps by introducing OpenFACADES, an open framework that leverages multimodal crowdsourced data to enrich building profiles with both objective attributes and semantic descriptors through multimodal large language models. Our methodology proceeds in three major steps. First, we integrate street-level image metadata from Mapillary with OpenStreetMap geometries via isovist analysis, effectively identifying images that provide suitable vantage points for observing target buildings. Second, we automate the detection of building facades in panoramic imagery and tailor a reprojection approach to convert objects into holistic perspective views that approximate real-world observation. Third, we introduce an innovative approach that harnesses and systematically investigates the capabilities of open-source large vision-language models (VLMs) for multi-attribute prediction and open-vocabulary captioning in building-level analytics, leveraging a globally sourced dataset of 30,180 labeled images from seven cities. Evaluation shows that fine-tuned VLM excel in multi-attribute inference, outperforming single-attribute computer vision models and zero-shot ChatGPT-4o. Further experiments confirm its superior generalization and robustness across culturally distinct regions and vary

关键词： Multi-task learning

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Handwritten Digit Recognition using Deep Learning Networks

Handwritten Digit Recognition using Deep Learning Networks

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Telecommunications, Optics and computer Science (TOCS), IEEE Conference on

作者： Shiying Yu Jinmei Liu Hang Shu Zhiwei Cheng School of Geodesy and Geomatics Wuhan University Wuhan China School of Arts and Sciences Rutgers University New Jersey NJ United States School of Computer and Information Science South West University Chongqing China Department of Architecture and Civil Engineering Xi’an University of Science and Technology Xi’an China

ISBN: (纸本)9781665470544

Handwritten digit recognition has been widely researched. Numerous classification methods have been proposed. This article improves the recognition of handwritten numbers method based on CNN neural network. Recognition of handwritten digits has made great progress, but due to insufficient recognition accuracy, it will have a certain impact on work efficiency. In this paper, a two-layer CNN network with two fully connected layers can achieve a good prediction performance. In our framework, the activation function used is the ReLU function, to some extent, this avoids the difficulties of gradient disappearance and gradient saturation. At the same time, dropout is added to the full connection layer to discard the neurons in the network with a certain probability, which can avoid certain accidents, and the final trained network will be more robust and the generalization performance will be stronger. This method effectively improves the accuracy of recognition in the final experiment, which achieves 98.75% accuracy on theMNIST dataset and 99.25% on the CIFAR-100 dataset.

关键词： Industries Deep learning Handwriting recognition Text recognition Neurons Predictive models Optics

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A Comprehensive Review on Big Data Challenges

A Comprehensive Review on Big Data Challenges

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Business Analytics for technology and Security (ICBATS), International Conference on

作者： Salil Bharany Nasser Taleb M. Tariq Sadiq Nayab Kanwal Mohamed Abdelhakim Taher M. Ghazal Manas Pradhan Ateeq Ur Rehman CET Department Tech Amritsar India UK Faculty of Management Canadian University Dubai Dubai UAE School of Architecture Engineering University of Brighton School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Engineering and IT University of Dubai School of Information Technology Skyline University College Sharjah UAE

Increased hardware capability has allowed many businesses to store massive amounts of data in the previous few years. Streams of data may go on forever, so they are used in so many different applications. Analyzing and querying these streams is challenging for a stream of dynamic information. The term "big data" refers to a collection of methods and tools that must be combined in novel ways to unearth the vast amounts of previously unseen information buried within vast, diverse, and intricate databases. There are several ways to obtain, organize and analyze data in the big data world. In order to deal with streaming data, efficient data mining procedures must be in place. In this article, we examine a variety of Big Data-related concerns and challenges. This paper introduces the features, and challenges of managing big data.

关键词： Planets Transforms Big Data Media Telecommunications Security Surges

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Big data-oriented energy prosumption service in smart community districts: a multi-case study perspective

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Energy Informatics 2019年第1期2卷 1-26页

作者： Anthony, Bokolo Petersen, Sobah Abbas Ahlers, Dirk Krogstie, John Livik, Klaus Department of Computer Science Norwegian University of Science and Technology NTNU TrondheimNO-7491 Norway Department of Architecture and Planning Norwegian University of Science and Technology Trondheim Norway Powel AS Klæbuveien 194 TrondheimNO-7037 Norway

The smart grid achieves bidirectional information and energy flow between energy consumer and utility grid, aiding energy users not only to utilize energy, but also to produce, sell, and share energy with other consumers or with the utility grid. This type of energy user is referred to as the "prosumer". Thus, prosumer management structures are important within energy market. However, prior studies on energy sustainability has paid little attention on prosumer involvement and management. Likewise, the continuous growth of cities has increased data processing complexity. Consequently, processing and analysis of historical, online, and real-time streaming data from energy sensors and metering devices has become a major issue in smart cities. Therefore, this research aims to present an architecture based on big data to improve energy prosumption in smart community districts by applying enterprise architecture approach grounded on The Open Group architecture Framework (TOGAF). Accordingly, qualitative methodology is adopted to collect data by employing case study by focus group interview from two energy companies in Norway to preliminarily validate the architecture. Findings from the case studies was demonstrated in ArchiMate modeling language to evaluate the applicability of the architecture. Moreover, findings from this study provides practical scenario that energy service providers can refer to in designing their own energy data platforms. Essentially, the architecture can be utilized as a guide to help municipalities and policy makers in creating approach for energy data analytics in smart community districts towards making decisions for future energy prosumption planning. © 2019, The Author(s).

关键词： Big data

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Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment

arXiv

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arXiv 2022年

作者： Ni, Haomiao Xue, Yuan Ma, Liya Zhang, Qian Li, Xiaoye Huang, Xiaolei College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Shenzhen Baoan Women’s and Children’s Hospital Jinan University Shenzhen China School of Information and Control Engineering Xi’an University of Architecture and Technology Xi’an China

General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can be applied to achieve good results in GMA, and more importantly, augmenting raw video with infant body parsing and pose estimation information can significantly improve performance. To solve the problem of efficiently utilizing partially labeled IMVs for body parsing, we propose a semi-supervised model, termed SiamParseNet (SPN), which consists of two branches, one for intra-frame body parts segmentation and another for inter-frame label propagation. During training, the two branches are jointly trained by alternating between using input pairs of only labeled frames and input of both labeled and unlabeled frames. We also investigate training data augmentation by proposing a factorized video generative adversarial network (FVGAN) to synthesize novel labeled frames for training. FVGAN decouples foreground and background generation which allows for generating multiple labeled frames from one real labeled frame. When testing, we employ a multi-source inference mechanism, where the final result for a test frame is either obtained via the segmentation branch or via propagation from a nearby key frame. We conduct extensive experiments for body parsing using SPN on two infant movement video datasets;on these partially labeled IMVs, we show that SPN coupled with FVGAN achieves state-of-the-art performance. We further demonstrate that our proposed SPN can be easily adapted to the infant pose estimation task with superior performance. Last but not least, we explore the clinical application of our method for GMA. We collected a new clinical IMV dataset with GMA annotations, and our experiments show that our SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset

关键词： Generative adversarial networks

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Smart Prediction Models for Energy Efficiency in Smart Residential Buildings: A Deep Learning Approach

Smart Prediction Models for Energy Efficiency in Smart Resid...

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Technological Engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： A. Vasantharaj M. Irshad Ahamed K. Prema Aditya Mudigonda J. B. Sapaev Kavitha Thandapani Electronics and Communication Engineering Kalaignar Karunanidhi Institute of Technology Information for Department of Electronics and Communication Engineerin E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India JNIAS School of Planning and Architecture Hyderabad Telangana India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and vgy Technology Avadi Chennai Tamil Nadu

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

Using power in houses has given rise to several ecological issues, including air pollution. Predicting building energy use is essential for better regulating or reducing energy usage. Machine Learning (ML) techniques have been used to estimate the power use of operating buildings. However, there has been little research on using ML algorithms to anticipate the prospective energy usage of buildings during the initial design phase. When lowering energy-inefficient buildings, it is crucial to tackle the underlying issue: accurately anticipating energy consumption before construction. This is necessary to prevent the future development of environmentally detrimental buildings. This research introduces a new and innovative Hybrid Deep Learning Model for Predicting Energy Efficiency in Smart Residential Buildings (HDL-PEE-SRB). The suggested framework has two steps: cleaning the information and constructing the model. The information cleansing step utilizes pre-processing methods to manipulate the original information and incorporates lag values as supplementary characteristics. During model-building, the hybrid model undergoes training using the processed data. The HDL model combines fully linked layers, Unidirectional Long Short-Term Memory (Uni-LSTM), and bi-directional LSTMs (Bi-LSTM), which are stacked together. The suggested model is assessed using standard energy usage databases, demonstrating higher accuracy than commonly used HDL models. The proposed model surpasses other HDL models, demonstrating the most favorable performance with a MAPE of 1.94% and an accuracy of 98.06%.

关键词： Deep learning Training Energy consumption Accuracy Biological system modeling Buildings Predictive models

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Emotional evaluation of architectural interior forms based on personality differences using virtual reality

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Frontiers of Architectural Research 2020年第1期9卷 138-147页

作者： Maryam Banaei Ali Ahmadi Klaus Gramann Javad Hatami School of Architecture and Environmental Design Iran University of Science and TechnologyTehranIran Faculty of Computer Engineering K.N.Toosi UniversityTehranIran Department of Psychology and Ergonomics Biological Psychology and NeuroergonomicsBerlin Institute of TechnologyBerlinGermany Center for Advanced Neurological Engineering University of CaliforniaSan DiegoCAUSA School of Software University of Technology SydneySydneyAustralia Department of Psychology University of TehranTehranIran

Recent studies have shown that architectural interior forms could impact the affective state of ***,the direct relation of specific forms with specific affective states is difficult to *** addition,no systematic categorization of architectural forms and their relation to emotional states *** investigation of the impact of architectural features on inhabitants'emotions is further complicated by the use of two-dimensional images of forms in laboratory investigations,which cannot perceive real-world ***,the interior form consists of a combination of different forms rather than only pure forms,which was considered in previous *** study aimed to rill these gaps by evaluating interior forms on the basis of clustering different images of built living rooms throughout history as well as their impact on *** study used pleasure,arousal,and dominance ratings with an emphasis on individual differences in *** sample rooms were created based on formal clusters of architectural *** showed a relationship between forms and emotional states for different personality *** work provided a novel approach on the influence of architecture on emotion by considering systematic form categorization and combinations,personality differences,and a virtual reality setup.

关键词： Architectural interior form PAD Self-assessment manikin Personality Virtual reality

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Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

arXiv

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arXiv 2021年

作者： Liang, Tianming Liu, Yang Liu, Xiaoyan Zhang, Hao Sharma, Gaurav Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. © 2021, CC BY.

关键词： Graphic methods

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Irreversibility analysis of magnetized flow of engine oil hybrid nanofluid (Ti6Al4V-ZnO/EO) over a stretching surface

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Alexandria Engineering Journal 2025年 127卷 214-227页

作者： Hussain, Syed M. Faridi, Aftab Ahmed Ahmad, Hijaz Ali, Kashif Iqbal, Muhammad Rashid Jamshed, Wasim Guedri, Kamel Almaliki, Abdulrazak H. Department of Mathematics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia Department of Mathematics The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Near East University Operational Research Center in Healthcare TRNC Mersin 10 Nicosia99138 Turkey Department of Technical Sciences Western Caspian University Baku1001 Azerbaijan Department of Mathematics College of Science Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Basic Sciences and Humanities Muhammad Nawaz Sharif University of Engineering and Technology Multan60000 Pakistan Department of Mathematics and Statistics Institute of Southern Punjab Multan32100 Pakistan Islamabad44000 Pakistan College of Engineering Al-Ayen Iraqi University An Nasiriyah64001 Iraq Department of Computer Engineering Biruni University Topkapi Istanbul Turkey Mechanical Engineering Department College of Engineering and Architecture Umm Al-Qura University P. O. Box 5555 Makkah21955 Saudi Arabia Department of Civil Engineering college of Engineering Taif University Taif21944 Saudi Arabia

This article analyzes the irreversibility of hydro-magnetic dynamics of hybridized nanofluid comprising of titanium alloy (Ti6Al4V) and zinc-oxide (ZnO) submerged into engine oil (used as a fuel) through a porous stretching sheet with multipart effects of viscous dissipation and non-uniform heat source. An induced magnetic field exists in the flow field due to the conducting nature of the hybrid nanofluid. The equations of the flow model are transformed by the similarity transformations and then simulated through a numerical scheme constructed with a formulation of central differences that adopts successive over-relaxation methodology. In this thermodynamic system, entropy and Bejan number profiles corresponding to the prominent parameters are simultaneously analysed for both the nanofluid case and hybrid nanofluid case in the Cartesian coordinates. A comparison table is constructed to validate the numerical results with existing literature. The impacts of various parameters on Nusselt number and skin friction coefficient are graphed with reference to the magnetic field parameter. A significant reduction in irreversibility and a remarkable improvement in thermal characteristics of engine oil are observed due to induced magnetism effects. The engine oil hybrid nanofluid (Ti6Al4V-ZnO/EO) exhibits a 13.89 % higher heat transfer rate over a porous stretching surface compared to the engine oil nanofluid (Ti6Al4V/EO). © 2025 The Authors

关键词： Magnetic levitation

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Exploring Spatial Hybrid User Interface for Visual Sensemaking

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Tong, Wai Li, Haobo Xia, Meng Kam-Kwai, Wong Pong, Ting-Chuen Qu, Huamin Yang, Yalong Nanchang University School of Mathematics and Computer Sciences Nanchang 330031 China Shenzhen University School of Architecture & Urban Planning Shenzhen 518060 China University of Calgary Department of Geomatics Engineering Calgary T2N 1N4 Canada Wuhan University School of Geodesy and Geomatics Wuhan 430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan 430079 China Finnish Geospatial Research Institute Department of Remote Sensing and Photogrammetry Masala 02430 Finland Wuhan University of Technology School of Safety Science and Emergency Management Wuhan 430079 China Nanjing University of Information Science & Technology School of Remote Sensing & Geomatics Engineering Nanjing 210000 China Nanjing Forestry University College of Civil Engineering Nanjing 210037 China Guangzhou Urban Planning & Design Survey Research Institute Guangzhou 51100 China

We built a spatial hybrid system that combines a personal computer (PC) and virtual reality (VR) for visual sensemaking, addressing limitations in both environments. Although VR offers immense potential for interactive data visualization (e.g., large display space and spatial navigation), it can also present challenges such as imprecise interactions and user fatigue. At the same time, a PC offers precise and familiar interactions but has limited display space and interaction modality. Therefore, we iteratively designed a spatial hybrid system (PC+VR) to complement these two environments by enabling seamless switching between PC and VR environments. To evaluate the system's effectiveness and user experience, we compared it to using a single computing environment (i.e., PC-only and VR-only). Our study results (N=18) showed that spatial PC+VR could combine the benefits of both devices to outperform user preference for VR-only without a negative impact on performance from device switching overhead. Finally, we discussed future design implications. © 1995-2012 IEEE.

关键词： Data visualization

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