{"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":17,"n_cols":5,"rows_kept":17,"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],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[14,2,14,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996565871903316"]
[null,null,"Titolo",null,"Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part II / / Xiaochun Yang [and seven others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2023]"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"3-031-46664-0"]
[null,null,"Edizione",null,"[First edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (722 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; Volume 14177"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Data mining"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Knowledge Graph -- Improving Distance Based Knowledge Graph Embedding via Contrastive Learning -- Knowledge Graph Completion with Information Adaptation and Refinement -- Duet Representation Learning with Entity Multi-Attribute Information in Knowledge Graphs -- TKGAT: Temporal Knowledge Graph Representation Learning Using Attention Network -- Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion -- Two Birds With One Stone: An Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition -- HEM: an improved parametric link prediction algorithm based on hybrid network evolution mechanism -- Joint embedding of local structures and evolutionary patterns for temporal link prediction -- Multimedia -- DQN-based Stitching Algorithm for Unmanned Aerial Vehicle Images -- HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for image classification -- DetOH: An Anchor-free Object Detector with Only Heatmaps -- LAANet: An Efficient Automatic Modulation Recognition Model Based on LSTM-Autoencoder and Attention Mechanism -- A Compact Phoneme-To-Audio Aligner for Singing Voice -- Song-to-Video Translation: Writing a Video from Song Lyrics Based on Multimodal Pre-Training -- Medical Image Analysis -- Breast Cancer Histopathology Image Classification Using Frequency Attention Convolution Network -- Wavelet-SVDD: Anomaly Detection and Segmentation with Frequency Domain Attention -- Anatomical-Functional Fusion Network for Lesion Segmentation using Dual-view CEUS -- SpMVNet: Spatial Multi-view Network for Head and Neck "]
[{"$nulls":4},"Organs at Risk Segmentation -- SNN-BS: A Clinical Terminology Standardization Method using Siamese Networks with Batch Sampling Strategy -- Natural Language -- Read then Respond: Multi-granularity Grounding Prediction for Knowledge-Grounded Dialogue Generation -- SUMO-PEGASUS: A Two-Stage Model for Long Text Summarization -- An Extractive Automatic Summarization Method for Chinese Long Text -- A Likelihood Probability-based Online Summarization Ranking Model -- Spatial Commonsense Reasoning for Machine Reading Comprehension -- Multimodal learning for Automatic Summarization: A Survey -- Deep Knowledge Tracing with Concept Trees -- Privacy and Security -- Cryptography-Inspired Federated Learning for Generative Adversarial Networks and Meta Learning -- A Hessian-Based Federated Learning Approach to tackle Statistical Heterogeneity -- Analyzing the Convergence of Federated Learning with Biased Client Participation -- Privacy Lost in online Education: Analysis of Web Tracking Evolution -- DANAA: Towards transferable attacks with double adversarial neuron attribution -- CRNN-SA: A Network Intrusion Detection Method Based on Deep Learning -- Applications -- Conspiracy Spoofing Orders Detection with Transformer-based Deep Graph Learning -- A Novel Explainable Rumor Detection Model with Fusing Objective Information -- VLS: A Reinforcement Learning-based Value Lookahead Strategy for Multi-product Order Fulfillment -- Deep Reinforcement Learning for Stock Trading with Behavioral Finance Strategy -- Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students -- How does ChatGPT Affect Fake News Detection Systems? -- CNGT: Co-attention Networks with Graph Transformer for Fact Verification -- Multi-Modal -- Supervised Discriminative Discrete Hashing for Cross-modal Retrieval -- A Knowledge-enhanced Inferential Network for Cross-modality Multi-hop VQA -- Multi-Head Similarity Feature Representation and Filtration for Image-Text Matching -- Multimodal Conditional VAE for Zero-shot Real-world Event Discovery -- MAMRP: Multi-modal Data Aware Movie Rating Prediction -- MFMGC: A Multi-Modal Data Fusion Model for Movie Genre Classification -- TED-CS: Textual Enhanced Sensitive Video Detection with Common Sense Knowledge."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining."]