| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910155574303321 |
|
|
Autore |
Keen Sam |
|
|
Titolo |
The power of stories workshops : Illuminating your life's meaning through stories. / / Sam Keen |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Solon, : Better Listen, 2015 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[Unabridged.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (3 audio files) : digital |
|
|
|
|
|
|
Classificazione |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
Soggetti |
|
Nonfiction |
New Age |
Sociology |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Audiolibro |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
|
|
|
|
|
Sommario/riassunto |
|
Sam Keen, bestselling author of Fire in the Belly has created a way to use stories to give shape and clarity to our personal destinies. Keen tells how to use story elements to reveal hidden potentials with step-by-step exercises to help us investigate the stories behind issues central to a greater self-understanding. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910878987403321 |
|
|
Autore |
Huang De-Shuang |
|
|
Titolo |
Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part I / / edited by De-Shuang Huang, Xiankun Zhang, Qinhu Zhang |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2024.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (521 pages) |
|
|
|
|
|
|
Collana |
|
Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 14875 |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Artificial intelligence |
Computers |
Computer networks |
Data mining |
Image processing - Digital techniques |
Computer vision |
Software engineering |
Artificial Intelligence |
Computing Milieux |
Computer Communication Networks |
Data Mining and Knowledge Discovery |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Software Engineering |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Intro -- Preface -- Organization -- Contents - Part I -- Machine Learning -- A Dynamic Collaborative Recommendation Method Based on Multimodal Fusion -- 1 Introduction -- 2 Related Works -- 2.1 Multi-modal Recommendation -- 2.2 Transformer for Recommendation -- 3 Methodology -- 3.1 Data Preprocessing and Feature Extraction -- 3.2 Bi-towernet -- 3.3 Deep Canonical Correlation Analysis -- 3.4 Short Term Recommendation Based on Dynamic Time Windows -- 3.5 |
|
|
|
|
|
|
|
|
|
Optimization -- 4 Experiments -- 4.1 Datasets -- 4.2 Metrics -- 4.3 Details -- 4.4 Results -- 5 Conclusion -- References -- Image Classification Using Graph Regularized Independent Constraint Low-Rank Representation -- 1 Introduction -- 2 Related Work -- 2.1 Low-Rank Representation -- 2.2 Manifold Learning for Graph Regularization -- 3 Proposed Method -- 3.1 Hilbert-Schmidt Independence Criterion -- 3.2 Model of GRI-LRR and Its Optimization Procedure -- 4 Experiments -- 4.1 Data Sets -- 4.2 Sensitivity Analysis of GRI-LRR -- 4.3 Experiment Results and Analysis -- 4.4 Ablation Analysis -- 5 Conclusion and Future Work -- References -- Identifying the Fraudulent Users for E-commerce Applications Based on the Access Behaviors -- 1 Introduction -- 2 Methodology -- 2.1 Behavior Feature Extraction -- 2.2 Feature Encoding and Compression -- 2.3 Spatial Relations and Identification -- 3 Experiments -- 3.1 Datasets -- 3.2 Hyperparameter Sensitivity Testing -- 3.3 Component Necessity Testing -- 3.4 Comparison with Baselines -- 3.5 Stability Assessment -- 4 Conclusion -- References -- LIFT: Discriminant Classification Approach of Malware Family on Time Consistent Open Set -- 1 Introduction -- 2 Related Work -- 2.1 Malware Open Set Recognition (MOSR) -- 2.2 Malware Recognition with Temporal Bias -- 3 Methodology -- 3.1 Overview -- 3.2 Feature Extraction Based on Linear Probe Boosted Swin Transformer. |
3.3 Feature Truncation Based on Distance -- 4 Experiment -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Experimental Settings -- 4.4 Baselines -- 4.5 Experimental Results -- 5 Conclusion -- References -- Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRL -- 1 Introduction -- 2 Related Work -- 2.1 Few-Shot Semantic Role Labeling -- 2.2 Structured Information in Language Models -- 2.3 Prompt-Based Learning -- 3 PromptSRL -- 3.1 Stage I: Predicate Disambiguation -- 3.2 Stage II: Role Retrieval -- 3.3 Stage III: Argument Labeling -- 3.4 Stage IV: Post-process -- 4 Experiment -- 4.1 Dataset -- 4.2 Setup -- 5 Results -- 5.1 LLMs' Potential -- 5.2 LLMs' Limitations -- 5.3 Impact of Exemplars -- 5.4 Comparison with Untrained Humans -- 5.5 Ablation Study -- 6 Conclusion -- References -- Cache Side-Channel Attacks Detection for AES Encryption Based on Machine Learning -- 1 Introduction -- 2 Background and Related Work -- 2.1 Cache Side-Channel Attack -- 2.2 Hardware Performance Counters -- 2.3 Related Work -- 3 Experiment Preparation -- 4 Finding the Critical Sampling Interval -- 5 Build Attack Detection -- 6 Conclusion and Discussion -- References -- WARM: An Interpretability Module with Weighted Association Rule Mining for Recommendation Systems -- 1 Introduction -- 2 Proposed Method -- 2.1 Theory -- 2.2 Weight Settings -- 2.3 Weighted Association Rule Mining: WARM -- 2.4 An Interpretability Module with WARM for Recommendation Systems -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Evaluation of Interpretability (RQ1) -- 3.3 Detailed Study (RQ2) -- 3.4 Case Study (RQ3) -- 4 Conclusions -- References -- Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with 0-Constraints -- 1 Introduction -- 2 Methodology -- 2.1 Problem Statement -- 2.2 The Proposed APALM+ Algorithm. |
3 Convergence Analysis -- 3.1 Monotonic Convergence -- 3.2 Global Convergence -- 4 Numerical Experiments -- 4.1 Baseline Methods -- 4.2 Experiments on Synthetic Data -- 4.3 Experiments on Real Data -- 5 Conclusion -- References -- Collaborative Filtering Algorithm Based on Contrastive Learning and Filtering Components -- 1 Introduction -- 2 Social Recommendation Prediction Method Based on Filtering Components and Contrastive Learning (FMPRec) -- 2.1 Problem |
|
|
|
|
|
|
|
Definition -- 2.2 Model Framework -- 2.3 Model Optimization -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Evaluation Metrics -- 3.3 Overall Performance Analysis -- 3.4 Sensitivity Analysis -- 3.5 Analysis of Factors Influencing -- 4 Conclusion -- References -- IBAS-SVM Rolling Bearing Fault Diagnosis Method Based on Empirical Modal Characteristics -- 1 Introduction -- 2 EEMD-FE-IBAS-SVM Model -- 3 Combined Feature Extraction -- 4 IBAS-SVM Fault Diagnosis Model -- 4.1 Information Sharing Characteristic -- 4.2 Adaptive Step Size -- 5 Experiments and Analysis of Results -- 5.1 Experiments on CWRU Datasets -- 5.2 Experiments on IMS Datasets -- 6 Conclusion -- References -- Using Graph Neural Network to Analyse and Detect Annotation Misuse in Java Code -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Annotation Usage Project Structure Graph(AUPSG) Generation -- 3.2 Structure-Aware GNN Based Model -- 3.3 Intergrated into IDE -- 4 Experiment -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Baselines -- 4.4 Effectiveness Evaluation in Test Dataset(RQ1) -- 4.5 Effectiveness Evaluation in Real World Project(RQ2) -- 4.6 Case Study -- 5 Conclusion -- References -- Graph Causal Contrastive for Partial Label Learning -- 1 Introduction -- 2 Related Work -- 2.1 Partial Label Learning -- 2.2 Causal Learning -- 3 Preliminaries -- 4 Method -- 4.1 Causal View of Data-Generating Process. |
4.2 Causal and Non-causal Subgraph Segmentation -- 4.3 Causal Contrastive Learning -- 4.4 Causal Prototype Disambiguation -- 5 Experiments -- 5.1 Performance Evaluation -- 5.2 Feature Visualization -- 5.3 Causal and Non-causal Subgraph Analysis -- 6 Conclusions and Future Work -- References -- A Stacking Ensemble Deep Learning Model for Stock Price Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 Stock Price Forecast -- 2.2 Ensemble Deep Learning -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Framework Overview -- 3.3 Gating Mechanism -- 3.4 First Base Learner -- 3.5 Second Base Learner -- 3.6 Meta Learner -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Evaluation -- 4.4 Implementation -- 4.5 Overall Comparison -- 4.6 Ablation Study -- 5 Conclusion -- References -- Smart Trading: A Novel Reinforcement Learning Framework for Quantitative Trading in Noisy Markets -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Overview of the Proposed Framework -- 3.2 The Customized Trading Environment -- 3.3 The Design of Trading Net with Discrete Features -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 The Adaptive Scaler in Customized Environment -- 4.3 The Comparison of Trading Performance -- 4.4 Daily Paper Trading in Live Market -- 4.5 The Importance of Using Discrete Features -- 5 Conclusion -- References -- Enhancing Image Captioning with Transformer-Based Two-Pass Decoding Framework -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overall Framework -- 3.2 Visual Encoder -- 3.3 Draft Decoder -- 3.4 Cross-Modality Fusion Module -- 3.5 Deliberation Decoder -- 3.6 Optimization Strategy -- 4 Experimental Setup -- 4.1 Datasets and Evaluation Metrics -- 4.2 Draft Models -- 4.3 Implementation Details -- 5 Experimental Results -- 5.1 Comparing with Single-Pass Decoding Baselines. |
5.2 Comparing with State-of-the-Art Methods -- 5.3 Ablation Study -- 5.4 Qualitative Analysis -- 6 Conclusion and Future Work -- References -- Ontology-Aware Overlapping Event Extraction -- 1 Introduction -- 2 Related Work -- 3 Framework -- 3.1 Ontology-Aware Semantic Encoder -- 3.2 Type Detection Decoder -- 3.3 Trigger Extractor -- 3.4 Argument Extractor -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Comparison Models -- 4.3 Implementation Details -- 4.4 Experimental Analysis -- 5 Conclusion -- References -- TVD-BERT: |
|
|
|
|
|
|
|
|
|
A Domain-Adaptation Pre-trained Model for Textural Vulnerability Descriptions -- 1 Introduction -- 2 Methodology -- 2.1 Pre-training Dataset -- 2.2 Vocabulary Expansion -- 2.3 Adaptation Pre-training in TVD -- 3 Evaluation -- 3.1 Domain Similarity Analysis -- 3.2 Evaluation of Internal Task -- 3.3 Evaluation of Downstream Tasks -- 4 Enhanced Representation in DAPT -- 4.1 Limitations of MLM Tasks -- 4.2 Double Lexicon Masking -- 4.3 Evaluation of the Method -- 5 Conclusion -- References -- Improving Large Language Models in Multi-party Conversations Through Role-Playing -- 1 Introduction -- 2 The Role-Playing Multi-party Conversation Framework -- 2.1 Phase 1: Turn-Taking Phase -- 2.2 Phase 2: Utterance Phase -- 3 Experiments -- 3.1 Turn-Taking Experiment -- 3.2 Utterance Generation Experiment -- 3.3 MPC Generation Experiment -- 4 Conclusion -- Appendix -- A Prompts Used in RPMPC and the Diversity Evaluation -- References -- Context Compression and Extraction: Efficiency Inference of Large Language Models -- 1 Introduction -- 2 Optimization Algorithm -- 2.1 Computing Self-information -- 2.2 Computing Mutual-Information -- 2.3 Combining SI and MI -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Performance Correlation with Compression Ratio -- 3.3 CCE Performance on Different Tasks -- 3.4 Parameters Tuning. |
3.5 Discussion. |
|
|
|
|
|
|
Sommario/riassunto |
|
This 6-volume set LNAI 14875-14880 constitutes - in conjunction with the 13-volume set LNCS 14862-14874 and the 2-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. The intelligent computing annual conference primarily aims to promote research, development and application of advanced intelligent computing techniques by providing a vibrant and effective forum across a variety of disciplines. This conference has a further aim of increasing the awareness of industry of advanced intelligent computing techniques and the economic benefits that can be gained by implementing them. The intelligent computing technology includes a range of techniques such as Artificial Intelligence, Pattern Recognition, Evolutionary Computing, Informatics Theories and Applications, Computational Neuroscience & Bioscience, Soft Computing, Human Computer Interface Issues, etc. . |
|
|
|
|
|
|
|
| |