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

Vercel News
Vercel News
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
T
Tailwind CSS Blog
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
人人都是产品经理
人人都是产品经理
V
V2EX
量子位
Last Week in AI
Last Week in AI
Jina AI
Jina AI
博客园 - 【当耐特】
爱范儿
爱范儿
宝玉的分享
宝玉的分享
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Hugging Face - Blog
Hugging Face - Blog
博客园 - 三生石上(FineUI控件)
有赞技术团队
有赞技术团队
小众软件
小众软件
IT之家
IT之家
博客园_首页
博客园 - 聂微东
S
SegmentFault 最新的问题
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗

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
Semantic web based Sensor Planning Services (SPS) for Sen...
P. Udayakumar, M. Indhumathi · 2012-07-23 · via cs.SE updates on arXiv.org

The Sensor Planning Service (SPS) is service model to define the web service interface for requesting user driven acquisitions and observation. It's defined by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) group to provide standardized interface for tasking sensors to allow to defining, checking, modifying and cancelling tasks of sensor and sensor data. The goal of Sensor Planning Service (SPS) of OGC - SWE is standardize the interoperability between a client and a server collection management environment. The Sensor Planning Service (SPS) is need to automate complex data flow in a large enterprises that are depend on live & stored data from sensors and multimedia equipment. The obstacle are faced in Sensor Planning Service (SPS) are (I) Observation from sensor at the right time and right place will be problem, (II) acquisition information(data) that are collected at a specific time and specific place will be problem. The above two obstacle are accomplished and obtained by the web based semantic technology in order to provide & apply the ontology based semantic rule to user driven a acquisitions and observation of Sensor Planning Service (SPS). The novelty of our approach is by adding the semantic rule to Sensor Planning Service model in SWE and we implemented Sensor Planning Service (SPS) with semantic knowledge based to achieve high standardized service model for Sensor Planning Service (SPS) of OGC - SWE.