Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

低阻经络研究III：对经络组织学本质的推断，杨威生，，《低阻经络研究Ⅰ》通过用4个皮肤电极的方法测量人体阻抗，发现在健康人体体表浅层存在着低电阻带：低阻经络。《低阻经络研究Ⅱ�

华理研究生大组会论文汇报： large Language Model + knowledge graph Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering（2023 arXiv） Generated Knowledge Prompting for Commonsense Reasoning（ACL） Unifying Large Language Models and Knowledge Graphs: A Roadmap（2023 arXiv 综述）

David Barber 的一本书，贝叶斯推理和机器学习

事件是一种重要的知识，近年来，越来越多的工作关注于从开放域或领域文本中抽取结构化事件知识。同时，除了本身就很困难的事件抽取任务之外，近年来，越来越多的研究者开始关注于事件的推理工作中。以下给出由复旦大学知识工厂给出的上下系列综述论文“事件抽取及推理”。欢迎相关研究人员下载学习。

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

深度共鸣论文 最近的论文包括神经符号推理，逻辑推理，视觉推理，自然语言推理以及其他与深度学习和推理相关的主题。 0调查或谈话 [1] Yoshua Bengio，从系统1深度学习到系统2深度学习 [2] Yann Lecun，自我监督学习 [3]用于算法推理的PetarVeličković图表示学习 1数学问题 [1] Saxton，David等。 分析神经模型的数学推理能力。 arXiv预印本arXiv：1904.01557（2019）。 [2] Ortega，Pedro A.等人。 顺序策略的元学习。 arXiv预印本arXiv：1905.03030（2019）。 [3] Lample，Guillaume和FrançoisCharton。 象征性数学的深度学习。 arXiv预印本arXiv：1912.01412（2019）。 [4]卓，陶和莫汉·坎坎哈利（Mohan K

Autonomous Cyber Deception: Reasoning, Adaptive Planning, and Evaluation of HoneyThings By 作者: Ehab Al-Shaer – Jinpeng Wei – Kevin W. Hamlen – Cliff Wang ISBN-10 书号: 3030021092 ISBN-13 书号: 9783030021092 Edition 版本: 1st ed. 2019 出版日期: 2019-01-02 pages 页数: (235 ) $84.99 This textbook surveys the knowledge base in automated and resilient cyber deception. It features four major parts: cyber deception reasoning frameworks, dynamic decision-making for cyber deception, network-based deception, and malware deception. An important distinguishing characteristic of this book is its inclusion of student exercises at the end of each chapter. Exercises include technical problems, short-answer discussion questions, or hands-on lab exercises, organized at a range of difficulties from easy to advanced,. This is a useful textbook for a wide range of classes and degree levels within the security arena and other related topics. It’s also suitable for researchers and practitioners with a variety of cyber security backgrounds from novice to experienced. Cover Front Matter Part I. Cyber Deception Reasoning Frameworks 1. Using Deep Learning to Generate Relational HoneyData 2. Towards Intelligent Cyber Deception Systems 3. Honeypot Deception Tactics Part II. Dynamic Decision-Making for Cyber Deception 4. Modeling and Analysis of Deception Games Based on Hypergame Theory 5. Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception Part III. Network-Based Deception 6. CONCEAL:A Strategy Composition for Resilient Cyber Deception: Framework, Metrics, and Deployment 7. WetShifter:A Comprehensive Multi-Dimensional Network Obfuscation and Deception Solution 8. Deception-Enhanced Threat Sensing for Resilient Intrusion Detection 9. HONEYSCOPE: IoT Device Protection with Deceptive Network Views Part IV. Malware Deception 10. gExtractor: Automated Extraction of Malware Deception Parameters for Autononous Cyber Deception 11. Malware Deception with Automatic Analysis and Generation of HoneyResource

对因果推理发展的研究广泛地集中于实现两个目标: 理解因果推理的起源，以及检验因果推理如何随着发展而变化。本书回顾了旨在实现这两个目标的证据和理论。

概率推理、智能系统，贝叶斯网、马尔科夫网方面的经典书