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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
PRESENT: An Android-Based Class Attendance Monitoring Sys...
Djoanna Marie V. Salac · 2020-11-20 · via cs.SE updates on arXiv.org

The study aimed to develop an Android-Based Class Attendance Monitoring Application using Face Recognition to make attendance checking and monitoring easier and faster. The researcher used incremental model as the software development process and the application was evaluated by seventeen (17) faculty members .A validated evaluation questionnaire was used to rate the level of acceptability of the application based on ISO 9126 software quality and the level of satisfaction for its major features. For the statistical treatment of the data collected, Likert Scale, weighted mean and t-test were utilized by the researcher. The results revealed that instructors find the existing way of checking attendance as time consuming and a tedious task. Furthermore, the respondents assessed the developed application as moderately acceptable in terms of functionality, reliability and usability while portability was rated as highly acceptable. With regards to the features, the respondents were very satisfied. The researcher concluded that the developed application was useful and it can support the needs of the instructors to make attendance checking and monitoring easier, faster, and reliable. Due to its acceptable evaluation result, instructors should consider the use of this tool as an alternative to the existing process of checking and monitoring class attendance. With the integration of different technologies such as Android, face recognition and SMS, the traditional way of checking class attendance can be made easier, faster, reliable and secured, thus improving classroom management.