Arama Sonuçları - Crowds and Power

Cyber-Physical Systems: Architecture, Security and Application

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8th International Conference on Engineering, Project, and Product Management (EPPM 2017) Proceedings /

İçindekiler: “…Confirmatory factor analysis of perceived risk factors for crowd safety in large buildings -- A Comparison of Paid vs Free Weather Services for Site Specific Weather Forecasts for Construction Projects -- Use of Ocean Sensors as Wave Power Generators -- Web-Based Intelligent RFID FacilitWeb-Based Intelligent RFID Facility Maintenance Systemsy Maintenance Systems -- Outsourcing Projects and Achieving the Organizational Goals Applied Study in Greater Amman Municipality.…”
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Civil society in comparative perspective

İçindekiler: “…[et al.] -- Making volunteering work: the power of voluntary organizations to enhance civic skills. …”
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Innovations in Computer Science and Engineering Proceedings of the Third ICICSE, 2015 /

İçindekiler: “…A Proposal for Searching Desktop Data -- Chapter 15. Low Power Analog Bus for System on Chip Communication -- Chapter 16. …”
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Deep Learning-Based Face Analytics. Yazar: Ratha, Nalini K.

İçindekiler: “…4.4 Improving Model Fidelity -- 4.4.1 Nonlinear 3DMM with Proxy and Residual -- 4.4.2 Global-Local-Based Network Architecture -- 4.5 Experimental Results -- 4.5.1 Ablation Study -- 4.5.2 Expressiveness -- 4.5.3 Representation Power -- 4.5.4 Applications -- 4.6 Conclusions -- References -- 5 Deblurring Face Images Using Deep Networks -- 5.1 Deep Semantic Face Deblurring -- 5.2 Deblurring Via Structure Generation and Detail Enhancement -- 5.3 Uncertainty Guided Multi-stream Semantic Networks -- 5.3.1 Image Deblurring Network -- 5.3.2 Semantic Segmentation Network (SN) -- 5.3.3 Base Network (BN) -- 5.3.4 UMSN Network -- 5.3.5 Loss for UMSN -- 5.3.6 Uncertainty Guidance -- 5.3.7 Experimental Results -- 5.4 Conclusion -- References -- 6 Blind Super-resolution of Faces for Surveillance -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Learning Invariant Features for Faces -- 6.4 Network Architecture -- 6.4.1 Encoder-Decoder -- 6.4.2 GAN for Feature Mapping -- 6.4.3 Loss Function -- 6.4.4 Training -- 6.5 Experiments -- 6.6 Conclusions -- References -- 7 Hashing A Face -- 7.1 Introduction -- 7.2 Unique Challenges of Hashing A Face -- 7.3 Strategies for Face Hashing -- 7.3.1 Data-Dependent Versus Data-Independent -- 7.3.2 Linear Versus Pivots-Based Hashing -- 7.3.3 Unsupervised Versus Supervised Hashing -- 7.3.4 Image Versus Set/Video Hashing -- 7.4 Face Recognition Tasks and Evaluation -- 7.4.1 Face Verification -- 7.4.2 Face Search -- 7.4.3 Evaluation Metrics -- 7.5 Face Datasets -- 7.5.1 IJB-A: IARPA Janus Benchmark A -- 7.5.2 IJB-B: IARPA Janus Benchmark B -- 7.5.3 IJB-C: IARPA Janus Benchmark C -- 7.5.4 UMD Faces -- 7.5.5 CASIA WebFace Dataset -- 7.6 Face Features -- 7.6.1 UMD Features: First Generation -- 7.6.2 UMD Features: Second Generation -- 7.6.3 UMD Features: Third Generation -- 7.7 Face Hashing Experiments -- 7.7.1 Experimental Settings.…”
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