惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

人人都是产品经理
人人都是产品经理
Stack Overflow Blog
Stack Overflow Blog
S
SegmentFault 最新的问题
博客园 - 司徒正美
aimingoo的专栏
aimingoo的专栏
U
Unit 42
GbyAI
GbyAI
B
Blog RSS Feed
博客园 - Franky
L
LangChain Blog
Hugging Face - Blog
Hugging Face - Blog
美团技术团队
The GitHub Blog
The GitHub Blog
Y
Y Combinator Blog
云风的 BLOG
云风的 BLOG
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园 - 三生石上(FineUI控件)
Microsoft Azure Blog
Microsoft Azure Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
G
Google Developers Blog
Last Week in AI
Last Week in AI
阮一峰的网络日志
阮一峰的网络日志
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Apple Machine Learning Research
Apple Machine Learning Research

cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Modeling System Events and Negative Events Using Thinging...
Sabah Al-Fedaghi · 2022-11-23 · via cs.SE updates on arXiv.org

This paper is an exploration of the ontological foundations of conceptual modeling that addresses the concept of events and related notions. Development models that convey how things change over space and time demand continued attention in systems and software engineering. In this context, foundational matters in modeling systems include the definition of an event, the types of events, and the kinds of relationships that can be recognized among events. Although a broad spectrum of research of such issues exists in various fields of study, events have extensive applicability in computing (e.g., event-driven programming, architecture, data modeling, automation, and surveillance). While these computing notions are diverse, their event-based nature lets us apply many of the same software engineering techniques to all of them. In this paper, the focus is on addressing the dynamic concepts of system events and negative events. Specifically, we concentrate on what computer scientists would refer to as an event grammar and event calculus. Analyzing the concept of event would further the understanding of the event notion and provide a sound foundation for improving the theory and practice of conceptual modeling. An event in computer science has many definitions (e.g., anything that happens, changes in the properties of objects, and the occurrence of and transition between states). This paper is based upon a different conceptualization using thinging machines and Lupascian logic to define negative events. An event is defined as a time penetrated domain s region, which is described in terms of things and five-action machines. Accordingly, samples from event grammar and event calculus are remodeled and analyzed in terms of this definition. The results point to an enriched modeling technique with an enhanced conceptualization of events that can benefit behavior modeling in systems.