{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":35,"n_cols":5,"rows_kept":35,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[20,2,20,3],[21,2,21,3],[25,2,25,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996466217403316"]
[null,null,"Titolo",null,"Neural Information Processing [[electronic resource] ] : 22nd International Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV / / edited by Sabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015"]
[null,null,"ISBN",null,"3-319-26561-X"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVII, 702 p. 257 illus.)"]
[null,null,"Collana",null,"Theoretical Computer Science and General Issues, , 2512-2029 ; ; 9492"]
[null,null,"Disciplina",null,"006.32"]
[null,null,"Soggetti",null,"Pattern recognition systems"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part IV -- Deep Feature-Action Processing with Mixture of Updates -- Abstract -- 1 Introduction -- 1.1 Actor-Critic and Neural Networks -- 1.2 Deep Learning and RL -- 1.3 Robot Homing -- 2 The Model -- 2.1 Model Architecture and Components -- 2.2 Actor-Critic Combined Network with Double Eligibility Traces -- 2.3 Mixing Gradient with Conjugate Gradient Updates -- 2.4 Deep Blended Actor-Critic Architecture -- 3 Experimental Results -- 3.1 Agent Learning Behavior and Convergence -- References -- Heterogeneous Features Integration via Semi-supervised Multi-modal Deep Networks -- 1 Introduction -- 2 Semi-Supervised Multi-Modal Deep Networks -- 2.1 Model Architecture -- "]
[{"$nulls":4},"2.2 Extracting Homogeneous Representations by Root Networks -- 2.3 Feature Fusion with Top Networks -- 3 Experiments -- 3.1 Datasets and Experiment Setup -- 3.2 Experimental Results -- 4 Conclusion -- References -- Multimodal Deep Belief Network Based Link Prediction and User Comment Generation -- 1 Introduction -- 2 Models -- 2.1 Restricted Boltzmann Machine -- 2.2 Deep Belief Network -- 2.3 Multimodal Deep Belief Networks -- 3 Methodology -- 3.1 Link Network Structure Features -- 3.2 User Comment Features -- 3.3 Discriminative Deep Belief Networks -- 3.4 Reconstructive Deep Belief Networks -- 4 Experiments and Analysis -- 4.1 Experiment Setup -- 4.2 Link Prediction Results and Analysis -- 4.3 User Comment Generation Results and Analysis -- 5 Conclusion -- References -- Deep Dropout Artificial Neural Networks for Recognising Digits and Characters in Natural Images -- 1 Introduction -- 2 Methodology -- 2.1 Restricted Boltzmann Machines -- 2.2 Deep Neural Networks -- 2.3 Dropout Method -- 3 Data Set Description -- 4 Application and Results -- 5 Conclusion -- References -- A Multichannel Deep Belief Network for the Classification of EEG Data."]
[{"$nulls":4},"1 Introduction -- 2 Deep Belief Networks -- 3 The Proposed Implementations of Multichannel DBN -- 4 Data Set -- 5 Experimental Results and Discussion -- 6 Conclusion -- References -- Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Human Activity Recognition with Convolutional Neural Networks -- 3.1 Convolutional Neural Networks -- 3.2 Convolutional Neural Network Architecture and Hyperparameters -- 4 Experiments -- 5 Conclusion -- References -- Concentration Monitoring with High Accuracy but Low Cost EEG Device -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Overall Structure of the Proposed System -- 2.2 Experiment Design -- 2.3 EEG Signal Processing -- 2.4 Candidate Features for Concentration Detection -- 3 Experimental Results -- 4 Conclusion -- Acknowledgements -- References -- Transfer Components Between Subjects for EEG-based Driving Fatigue Detection -- 1 Introduction -- 2 Algorithm Description -- 2.1 Feature Extraction -- 2.2 Transfer Component Analysis -- 3 Experimental Setup -- 3.1 Subjects and Procedure -- 3.2 Data Collection and Pre-processing -- 3.3 Feature Smooth -- 3.4 Feature Extraction -- 3.5 Fatigue Measurement -- 3.6 Detailed Parameters for Training -- 4 Experiment Results -- 5 Conclusion and Future Work -- References -- A Proposed Blind DWT-SVD Watermarking Scheme for EEG Data -- 1 Introduction -- 2 The Proposed EEG Watermarking Approach -- 2.1 EEG Watermarking Embedding -- 2.2 EEG Watermarking Extraction -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Results -- 4 Conclusion and Future Work -- References -- A Study to Investigate Different EEG Reference Choices in Diagnosing Major Depressive Disorder -- Abstract -- 1 Introduction -- 2 Proposed EEG Measures -- 2.1 Inter-hemispheric Asymmetry -- 2.2 Inter-hemispheric Coherence."]
[{"$nulls":4},"2.3 Power Computation Based on Welch Periodogram Method -- 3 Participant Recruitment and Experiment Design -- 3.1 Study Participants -- 3.2 Experiment Design -- 4 Data Analysis -- 4.1 EEG Data Preprocess -- 4.2 EEG Analysis -- 4.3 Validation -- 5 Results -- 5.1 Calculations of Accuracy, Sensitivity, and Specificity -- 5.2 Low Dimensional Representation -- 5.3 Classification Results -- 6 Conclusions -- References -- Prosthetic Motor Imaginary Task Classification Based on EEG Quality Assessment Features -- 1 Introduction -- 2 Methodology -- 2.1 Signal Recording -- 2.2 Feature Extraction -- 2.3 Classification Model -- 3 Results -- 4 Conclusion and Future Work -- References -- Enhancing Performance of EEG-based "]
[{"$nulls":4},"Emotion Recognition Systems Using Feature Smoothing -- 1 Introduction -- 2 The EEG-based Emotion Recoginition Model with Feature Smoothing -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Feature Extraction -- 3.3 Feature Smoothing -- 3.4 Experimental Results -- 4 Conclusion and Future Work -- References -- Intelligent Opinion Mining and Sentiment Analysis Using Artificial Neural Networks -- 1 Introduction -- 2 The State of the Art -- 3 Description of the System -- 4 Conclusions and Perspectives -- References -- Mining Top-k Minimal Redundancy Frequent Patterns over Uncertain Databases -- 1 Introduction -- 2 Preliminaries and Problem Definition -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusion -- 6 Related Work -- References -- Exploring Social Contagion in Open-Source Communities by Mining Software Repositories -- 1 Introduction -- 2 Related Work and Research Method -- 3 Results and Discussions -- 3.1 Power Law Distribution -- 3.2 Best Time to Start New Projects -- 3.3 Social Contagion in Open-Source Software Development -- 4 Limitations and Future Work -- 5 Conclusion -- References."]
[{"$nulls":4},"Data Mining Analysis of an Urban Tunnel Pressure Drop Based on CFD Data -- 1 Introduction -- 2 Methodology -- 3 Experimental Result -- 4 Conclusion -- References -- MapReduce-based Parallelized Approximation of Frequent Itemsets Mining in Uncertain Data -- Abstract -- 1 Introduction -- 2 Related Work -- 3 MapReduce-based Parallel Approximation Algorithm -- 4 Experimental Results -- 4.1 Performance Analysis -- 4.2 Performance Comparisons -- 5 Conclusions -- Acknowledgement -- References -- A MapReduce Based Technique for Mining Behavioral Patterns from Sensor Data -- 1 Introduction -- 2 RFSPs Mining Problem in Wireless Sensor Networks -- 3 RFSPs Mining Using MapReduce Model -- 4 Experimental Results -- 5 Conclusion -- References -- A Methodology for Synthesizing Interdependent Multichannel EEG Data with a Comparison Among Three Blind Source Separation Techniques -- 1 Introduction -- 2 Construction of Synthetic Data -- 3 Evaluation of the Projection Techniques -- 4 Conclusion -- References -- Analysing the Robust EEG Channel Set for Person Authentication -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Dataset -- 3.2 Preprocessing and Feature Extraction -- 3.3 Channel Selection Criteria -- 4 Experimental Details -- 5 Results and Discussion -- 6 Conclusion and Future Work -- References -- Automatic Brain Tumor Segmentation in Multispectral MRI Volumetric Records -- 1 Introduction -- 2 Materials and Methods -- 2.1 BRATS Data Sets -- 2.2 The FCM Cascade -- 2.3 FCM Initialization -- 2.4 Decision Support -- 2.5 Evaluation of Accuracy -- 3 Results and Discussion -- 4 Conclusion -- References -- Real-Time EEG-based Human Emotion Recognition -- Abstract -- 1 Introduction -- 2 Background -- 3 Real-Time Emotion Recognition -- 3.1 Experimental-Design -- 3.2 Emotion Recognition Algorithm -- 4 Comparison and Results -- 5 Software and Tools."]
[{"$nulls":4},"6 Conclusion -- References -- Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data -- 1 Introduction -- 2 The Proposed NeuCube-Based Spiking Neural Network Methodology for Learning, Visualization and Clustering of STBD -- 2.1 Spiking Neural Networks for Modelling STBD -- 2.2 The NeuCube Architecture [3] -- 2.3 3D Dynamic Neuronal Clustering in a NeuCube SNN Model -- 3 Application of the Proposed Method on a Benchmark fMRI STBD -- 3.1 FMRI Data Acquisition Description -- 3.2 FMRI Data Mapping, Learning and Visualization in a SNNc -- 3.3 Dynamic Cluster Evolution in a NeuCube Model on the fMRI Case Study STBD -- 4 Conclusion -- References -- Vigilance Differentiation from EEG Complexity Attributes "]
[{"$nulls":4},"-- 1 Introduction -- 2 Subjects and Experimental Environment -- 3 Methodology -- 4 Results -- 4.1 Conclusion -- References -- Robust Discriminative Nonnegative Patch Alignment for Occluded Face Recognition -- 1 Introduction -- 2 Robust Discriminative Nonnegative Patch Alignment (RD-NPA) -- 2.1 Part Optimization -- 2.2 Whole Alignment -- 2.3 Objective Function of RD-NPA -- 3 Algorithm for Robust Discriminative Nonnegative Patch Alignment -- 4 Experiments -- 4.1 Data and Parameter Setting -- 4.2 Experimental Results -- 5 Conclusion -- References -- Single-Image Expression Invariant Face Recognition Based on Sparse Representation -- 1 Introduction -- 2 Shape-Constrained Sparse Representation -- 2.1 Shape-Constrained Texture Matching -- 2.2 Feature Interpretation -- 2.3 Sparse Texture Representation (STR) -- 3 Experiments -- 3.1 Shape Change -- 3.2 Expression Impact -- 4 Conclusion -- References -- Intensity-Depth Face Alignment Using Cascade Shape Regression -- 1 Introduction -- 2 Framework -- 2.1 Problem Description -- 2.2 Framework Structure -- 2.3 Feature -- 2.4 Initial Estimation."]
[{"$nulls":4},"3 Experiments."]
[null,null,null,"Sommario/riassunto","The four volume set LNCS 9489, LNCS 9490, LNCS 9491, and LNCS 9492 constitutes the proceedings of the 22nd International Conference on Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The 4 volumes represent topical sections containing articles on Learning Algorithms and Classification Systems; Artificial Intelligence and Neural Networks: Theory, Design, and Applications; Image and Signal Processing; and Intelligent Social Networks."]