LEADER 01091nam0 2200265 i 450 001 VAN0043481 005 20101103120000.0 010 $a88-374-1623-7 100 $a20060405d1998 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aSonetti$ein morte di Francesco Ferrante d'Avalos marchese di Pescara$fVittoria Colonna$gedizione del ms. 13. G.43 della Biblioteca nazionale di Napoli a cura di Tobia R. Toscano 210 $aMilano$cG. 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Hunter, Seth G. Jones 205 $a1st ed. 210 $aSanta Monica, CA $cRAND Corporation$d2006 215 $a1 online resource (85 p.) 300 $a"MG-146/2." 311 $a0-8330-3811-7 320 $aIncludes bibliographical references (p. 69-72). 327 $aBorder arrangements -- International force -- Palestinian military forces -- Israeli settlements -- Intelligence, monitoring, enforcement, and dispute -- Resolution provisions -- Special security issues regarding Jerusalem -- External security environment -- Conclusion -- Appendix -- A. security issues and the Arab-Israeli peace process, 1967/2003 -- B. "Clinton parameters" (presented by President Bill Clinton to the Israeli and -- Palestinian negotiators on December 23, 2000). 330 $aThis study examines key external security issues that must be met for there to be a successful independent Palestinian state following a peace agreement with Israel. It makes proposals for an international (NATO-led) peace-enabling force, Palestinian security forces, and liaison and confidence-building cooperation between Palestine and Israel. 606 $aArab-Israeli conflict$y1993-$xPeace 606 $aPalestinian Arabs$xPolitics and government 606 $aNational security$zIsrael 615 0$aArab-Israeli conflict$xPeace. 615 0$aPalestinian Arabs$xPolitics and government. 615 0$aNational security 676 $a956.05/3 700 $aHunter$b Robert Edwards$f1940-$0935276 701 $aJones$b Seth G.$f1972-$0913846 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143270103321 996 $aBuilding a successful Palestinian state$92106449 997 $aUNINA LEADER 13236nam 22008295 450 001 9910881098903321 005 20240818130234.0 010 $a9783031683091$b(electronic bk.) 010 $z9783031683084 024 7 $a10.1007/978-3-031-68309-1 035 $a(MiAaPQ)EBC31606752 035 $a(Au-PeEL)EBL31606752 035 $a(CKB)34075068500041 035 $a(DE-He213)978-3-031-68309-1 035 $a(EXLCZ)9934075068500041 100 $a20240818d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDatabase and Expert Systems Applications $e35th International Conference, DEXA 2024, Naples, Italy, August 26?28, 2024, Proceedings, Part I /$fedited by Christine Strauss, Toshiyuki Amagasa, Giuseppe Manco, Gabriele Kotsis, A Min Tjoa, Ismail Khalil 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (289 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14910 311 08$aPrint version: Strauss, Christine Database and Expert Systems Applications Cham : Springer,c2024 9783031683084 327 $aIntro -- Preface -- Organization -- Abstracts of Keynote Talks -- Multimodal Deep Learning in Medical Imaging -- Digital Humanism as an Enabler for a Holistic Socio-Technical Approach to the Latest Developments in Computer Science and Artificial Intelligence -- Deep Entity Processing in the Era of Large Language Models: Challenges and Opportunities -- Contents - Part I -- Contents - Part II -- Financial and Economic Data Analysis -- CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market -- 1 Introduction -- 2 Related Work -- 2.1 Retrieval Augmented Generation -- 2.2 Dense Retrievers -- 2.3 Specialised Financial Datasets -- 3 The Policy Retrieval Dataset for Stock Market in China -- 3.1 Data Collection -- 3.2 Data Processing -- 3.3 Unsupervised MoE Selection -- 3.4 Expert Annotation -- 3.5 Dataset Release -- 4 CSPR-MQA Pre-training -- 4.1 Data Preprocessing -- 4.2 Encoding -- 4.3 Decoding -- 5 Experiments -- 5.1 Models -- 5.2 Evaluation Metrics -- 5.3 Results and Analysis -- 6 Limitations -- 6.1 Compromise Between Labor Costs and Decision Comprehensiveness -- 6.2 Limited Experiments on Pre-Trained Language Models -- 7 Conclusion -- References -- Leveraging Heterogeneous Text Data for Reinforcement Learning-Based Stock Trading Strategies -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Terms -- 3.2 Problem Definition -- 3.3 Base Model -- 4 Proposed Method -- 4.1 Encording -- 4.2 Feature Generation -- 4.3 Action Determination -- 5 Experiment -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 5.4 Comparison with Index-Based Method -- 5.5 Effects of Periods and Initial Assets -- 6 Conclusion -- References -- TCMIDP: A Comprehensive Database of Traditional Chinese Medicine for Network Pharmacology Research -- 1 Introduction -- 2 Database Contents and Access -- 3 Data Mining -- 4 User Evaluation. 327 $a5 Conclusion -- References -- Graph Theory and Network Analysis -- Fast Subgraph Search with Graph Code Indices -- 1 Introduction -- 2 Preliminaries -- 3 Related Work -- 4 Basic Concept of the Proposed Method -- 5 Graph Representation and Indexing of Databases -- 6 Subgraph Search with the Code Tree -- 7 Experimental Evaluation -- 7.1 Experimental Settings -- 7.2 Experimental Results -- 8 Conclusion -- References -- Completing Predicates Based on Alignment Rules from Knowledge Graphs -- 1 Introduction -- 2 Motivating Example -- 3 The SYRUP Approach -- 4 Experimental Study -- 5 Related Work -- 6 Conclusions and Future Work -- References -- Enriching Hierarchical Navigable Small World Searches with Result Diversification -- 1 Introduction -- 2 Preliminaries and Related Work -- 3 Material and Methods -- 4 Empirical Evaluation -- 5 Conclusions -- References -- An Efficient Indexing Method for Dynamic Graph kNN -- 1 Introduction -- 1.1 Existing Approaches and Challenges -- 1.2 Our Approaches and Contributions -- 2 Preliminary -- 2.1 Problem Definition -- 2.2 Previous Method: CT Index -- 3 Proposed Method: Dynamic CT -- 3.1 Ideas -- 3.2 Adding Nodes and Edges -- 3.3 Removing Nodes and Edges -- 3.4 Complexity Analysis -- 4 Experimental Evaluation -- 4.1 Efficiency for Updating -- 4.2 Efficiency for Adding/Removing Edges -- 5 Conclusion -- References -- Database Management and Query Optimization -- Improving the Accuracy of Text-to-SQL Tools Based on Large Language Models for Real-World Relational Databases -- 1 Introduction -- 2 Related Work -- 3 A Real-World Benchmark for the Text-to-SQL Task -- 3.1 The Real-World Relational Database -- 3.2 The Sets of Views -- 3.3 The Test Questions and Their Ground Truth SQL Translations -- 4 The Proposed RAG-Based Technique -- 4.1 Generation of the Synthetic Dataset -- 4.2 The Proposed RAG-Based Techniques. 327 $a5 Experiments with an RW-RDB -- 5.1 Experimental Setup -- 5.2 Results -- 6 Experiments with Mondial -- 7 Conclusions -- References -- QPSEncoder: A Database Workload Encoder with Deep Learning -- 1 Introduction -- 2 Related Work -- 3 QPSEncoder Framework -- 3.1 Physical Plan Encoding -- 3.2 SQL Query Encoding -- 3.3 Database Schema Encoding -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Effectiveness of QPSEncoder on Numeric Predicates. -- 4.3 Effectiveness of QPSEncoder on Mixed Predicates. -- 4.4 Effectiveness of Model Components -- 5 Conclusion -- References -- Efficient Random Sampling from Very Large Databases -- 1 Introduction -- 2 Related Work -- 3 The Proposed Algorithms -- 3.1 Random Sampling in B+Tree of Height Three -- 3.2 Random Sampling in B+Tree of Height Four -- 3.3 Generalization to Any B+Tree Height -- 4 Analysis -- 5 Simulation Study -- 5.1 Experiments Framework -- 5.2 Experiments Setup -- 5.3 Implementation Details -- 5.4 Results -- 6 Summary and Future Work -- References -- SQL-to-Schema Enhances Schema Linking in Text-to-SQL -- 1 Introduction -- 2 Related Work -- 2.1 Customized Machine Learning Fine-Tuned Methods -- 2.2 Stimulating General LLM with Prompting -- 3 Methodology -- 3.1 Evaluation Metrics -- 3.2 Introduction to Each Module -- 4 Experiments and Analysis -- 4.1 Experiment One -- 4.2 Experiment Two -- 4.3 Experiment Three -- 5 Conclusion -- References -- Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data -- 1 Introduction -- 2 Preliminary -- 3 Algorithm Based on Interval Forest -- 3.1 Data Structure and Construction -- 3.2 Query Processing Algorithm -- 4 Algorithm Based on a Variant of Segment Tree -- 4.1 Variant of Segment Tree and Its Construction -- 4.2 Query Processing Algorithm -- 5 Conclusion -- References -- A Hierarchical Storage Mechanism for Hot and Cold Data Based on Temperature Model. 327 $a1 Introduction -- 2 Related Work -- 3 System Architecture -- 3.1 Data Temperature Model -- 3.2 Data Hierarchical Storage Mechanism -- 4 Experimentation and Analysis -- 4.1 Experimental Environment and Configuration -- 4.2 Experiment on Local Hot and Cold Data Migration Management -- 4.3 Experiment on Local and Remote Data Migration Management -- 5 Conclusion -- References -- Machine Learning and Large Language Models -- A Pre-trained Knowledge Tracing Model with Limited Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Transformer-Based Knowledge Tracing Model -- 3.2 BERT-Based Knowledge Tracing Model -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Experiment Results and Analysis -- 5 Conclusions and Future Work -- References -- Chorus: More Efficient Machine Learning on Serverless Platform -- 1 Introduction -- 2 Background and Motivation -- 3 Lambda Synchronous Parallel (LSP) Model -- 4 Buffering in Parameter Server Model -- 5 Design of Chorus -- 5.1 Architecture Overview -- 6 Evaluation -- 6.1 Methodology -- 6.2 Lambda Synchronous Parallel (LSP) Model -- 6.3 Buffering System -- 6.4 Comparison -- 7 Conclusion -- References -- Evaluating Performance of LLaMA2 Large Language Model Enhanced by QLoRA Fine-Tuning for English Grammatical Error Correction -- 1 Introduction -- 2 Related Work -- 2.1 Grammar Error Correction -- 2.2 LLaMA2 Large Language Model -- 3 Methodology -- 3.1 Data Preparation -- 3.2 Prompts and Learning Strategies -- 3.3 Parameter Efficient Fine-Tuning via QLoRA -- 4 Experiments -- 4.1 Text Generation Settings -- 4.2 Parameter Settings of Training -- 4.3 Experimental Results -- 4.4 Error Type Analysis -- 4.5 Performance Evaluation of Open Source Language Tools -- 5 Conclusion -- References -- A Label Embedding Algorithm Based on Maximizing Normalized Cross-Covariance Operator -- 1 Introduction. 327 $a2 Preliminaries -- 3 The Proposed Method -- 4 Experiments -- 4.1 Benchmark Data Sets and Evaluation Metrics -- 4.2 Compared Methods and Experimental Settings -- 4.3 Performance Evaluation and Analysis -- 5 Conclusion -- References -- Analyzing the Efficacy of Large Language Models: A Comparative Study -- 1 Introduction -- 2 Literature Review -- 3 Datasets and Preprocessing -- 3.1 Dataset Construction by Question-Answer Pair Generation -- 3.2 Fine-Tuning the LLM -- 3.3 Document Parsing and Text Comprehension -- 4 Methodology -- 4.1 Integrated Evaluation: BLEU, ROUGE, and Cosine Similarity -- 4.2 Accuracy Assessment Through Z-Score Outlier Detection -- 5 Observations and Results -- 5.1 Classification of Errors -- 5.2 Comparison of Performance of Our Framework -- 6 Conclusion and Future Directions -- References -- Leveraging Large Language Models for Flexible and Robust Table-to-Text Generation -- 1 Introduction -- 2 Related Work -- 3 Methods and Experiment Settings -- 4 Experiments Results -- 5 Conclusion -- References -- Recommender Systems and Personalization -- Collaborative Filtering for the Imputation of Patient Reported Outcomes -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 MDASI-HN Data -- 3.2 Collaborative Filtering (CF) for MDASI-HN -- 4 Evaluation -- 4.1 Evaluation Metrics: -- 5 Experimental Results -- 5.1 Experimental Setup -- 5.2 Data Statistics -- 5.3 CF Techniques Comparison -- 5.4 Effect of k on CF-SYM-PCC Imputation -- 5.5 Comparing CF-SYM-PCC Against Other Methods -- 5.6 Comparing CF-SYM-PCC and LI Techniques Per Symptom -- 5.7 PCC Correlation Symptom Clusters -- 6 Conclusion -- References -- Category-Aware Sequential Recommendation with Time Intervals of Purchases -- 1 Introduction -- 2 Method -- 2.1 Problem Setup -- 2.2 Category and Time Interval Aware Sequence Recommendation -- 2.3 Framework of the Dual Model. 327 $a3 Performance Evaluation. 330 $aThe two-volume set LNCS 14910 and 14911 constitutes the proceedings of the 35th International Conference on Database and Expert Systems Applications, DEXA 2024, which took place in Naples, Italy, in August 2024. The 27 full and 20 short papers included in the proceedings set were carefully reviewed and selected from 102 submissions. They were organized in topical sections as follows: Part I: Financial and economic data analysis; graph theory and network analysis; database management and query optimization; machine learning and large language models; recommender systems and personalization; Part II: Blockchain and supply management; data mining and knowledge discovery; spatiotemporal data and mobility analysis; computer vision and image processing; data security and privacy; database indexing and query processing; specialized applications and case studies. . 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14910 606 $aDatabase management 606 $aArtificial intelligence 606 $aInformation technology$xManagement 606 $aSoftware engineering 606 $aInformation storage and retrieval systems 606 $aData mining 606 $aDatabase Management 606 $aArtificial Intelligence 606 $aComputer Application in Administrative Data Processing 606 $aSoftware Engineering 606 $aInformation Storage and Retrieval 606 $aData Mining and Knowledge Discovery 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aInformation technology$xManagement. 615 0$aSoftware engineering. 615 0$aInformation storage and retrieval systems. 615 0$aData mining. 615 14$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aComputer Application in Administrative Data Processing. 615 24$aSoftware Engineering. 615 24$aInformation Storage and Retrieval. 615 24$aData Mining and Knowledge Discovery. 676 $a005.74 700 $aStrauss$b Christine$01423736 701 $aAmagasa$b Toshiyuki$01423737 701 $aManco$b Giuseppe$01765447 701 $aKotsis$b Gabriele$0848995 701 $aTjoa$b A. Min$0909730 701 $aKhalil$b Ismail$01379892 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910881098903321 996 $aDatabase and Expert Systems Applications$94207155 997 $aUNINA