Aug 2026· Journal Computer Science and Applied Technology· 0 citations
TL;DR
The implementation results indicate that HadiRin can support a more measurable attendance process and improve attendance accountability through the combination of location verification and selfie capture and further development can focus on strengthening system security through location anti-spoofing mechanisms and liveness detection.
Abstract
Paper-based attendance and conventional systems such as cards, RFID, and fingerprint devices still have limitations in preventing attendance fraud, verifying employee presence in real time, and supporting workers who operate outside a single attendance-machine location. This study aims to design and develop HadiRin, a web-based digital attendance system that combines location verification and selfie capture in a single attendance flow, while also providing digital leave and permission submission and approval by HRD/Admin. The software was developed using the Prototyping method through requirements gathering, rapid design, prototype evaluation, revision, and implementation. The system uses the browser Geolocation API and MediaDevices API on the client side and a relational database on the server side. Functional evaluation using black-box testing based on scenarios and data obtained from actual system usage showed that multi-role authentication, check-in/check-out, location validation, photo capture, leave submission, HRD/Admin approval, and employee data management operated as expected. The implementation results indicate that HadiRin can support a more measurable attendance process and improve attendance accountability through the combination of location verification and selfie capture. Further development can focus on
strengthening system security through location anti-spoofing mechanisms and liveness detection.
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James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
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