Sidhesh Badrinarayan

The world has moved to smartphones and retaining users on applications is of prime importance to developers. When it comes to engagement and retention, being able to segment users by how often they visit the application, and how long their sessions are, can help developers understand what is working within the application. Techniques like A/B testing and custom event tracking can help consociate the user with the developer. It will also help them analyze the behavior of various users to find the most engaged user and see the commonalities of these user segments from historical data. The usage data is tracked from android devices and is sent to MongoDB which is a NoSQL document oriented database. This data is selectively sent to Hadoop for processing. Final results are stored in MySQL. These results can be viewed by developers through a web interface which has powerful data visualization tools. The developers will also have the freedom to perform application specific analytics.

Gestures are a natural way for humans to interact. We propose a gesture recognition library using the motion sensors embedded on mobile devices. Data from a combination of sensors form a signature for each individual gesture which is then recognized using pattern recognition technique. Gesture recognition is achieved in the video game console market using Kinect/camera or by controllers like Wii, Xbox controller etc. These devices are generally costly and have limitations associated with them. The objective of our idea is to provide an amazing gaming experience, generally associated with costly devices, using smartphones that are already widely available. The library can be used in applications catering to domains like gaming, augmented reality, browsing applications and many more. Due to the rising popularity of smartphones, the reach of our idea will be huge. Consequently, smart phones can soon replace other gaming consoles, and our system can aid this transition.

Enterprise compliance documentation is often written after systems have already changed, which leaves policy narratives, architecture diagrams, runbooks, and audit evidence out of sync with executable behavior. This paper proposes Retrieval-Grounded Documentation Agents (RGDA), a synthetic architecture in which bounded documentation agents maintain compliance text by combining abstract-syntax-tree-aware reflexion, hybrid semantic-relational evidence packs, distributed retrieval-augmented generation, model-context-protocol schemas, anonymized evidence views, and legal-meme lineage scoring. RGDA extends the policy-verified governance fabric of Policy-Verified Agentic DataOps for Regulated Multi-Cloud Analytics and the telemetry-to-evidence loop of Low-Latency Grid Intelligence with Self-Governing Stream and Calibration Agents by treating documentation edits as governed actions that must cite executable, operational, and policy evidence before they can be accepted. We define the architecture, evidence model, documentation-maintenance algorithm, and a simulated compliance benchmark over software, cloud, and grid-observability workflows. In simulation, RGDA improves evidence-supported claim accuracy from 73.2% to 94.8%, reduces stale compliance claims by 64.7%, and eliminates unauthorized sensitive-evidence exposure relative to an unbounded documentation-agent baseline. These results are hypothetical and are intended to make the design auditable rather than to claim production deployment.