Align already gathered (RFP Reputation & Voting Weight System) requirements and examine the proposed high-level architecture design and map to reassessed stakeholders’ needs. Establish the core RVWMP with detailed specifications for VEs and their integration. Key is data federation and VE Creation for modularity and extensibility of the architecture with prioritized user privacy and data control and support of flexible reputation scoring mechanisms.

D1 Project Plan/Architectural blueprint document outlining MIMMS implementation VE specifications (data models interfaces) communication protocols and scalability strategy. In RVWMP the reputation score is not just a numerical value but rather a contextualized property of a VE that exists within a governance structure. Each Real Entity (RE) has exactly one Master VE which acts as the authoritative controller for its subordinate VEs. The Master VE ensures identity verification role-based governance enforcement fault tolerance and secure data synchronization across the distributed VE ecosystem complying with governance constraints preventing unauthorized actions while enabling fine-grained role-based interactions. When two subordinated VEs (belonging to the same RE) need to sync the Master VE acts as a STUN/TURN server ensuring secure routing and identity verification before granting direct P2P connections. Once authorized the subordinated VEs merge their embedded Dgraph databases ensuring state consistency. The Master VE keeps a reference to the last known state of each subordinated VE. If a subordinated VE goes offline the Master VE ensures seamless reintegration when it reconnects and the ledger mantains the governance state. The Master VE only records governance-wide actions in the ledger (e.g. system-wide policy updates compliance validation third-party interactions). Private transactions between an RE’s VEs remain inside the distributed VE network.

$9,000 USD

1. Completion of Architectural Blueprint: Document outlining MIMMS implementation, VE specifications, and communication protocols (100% completion). 2. VE Specifications: Detailed data models and interfaces for core VEs (Voter, Proposal, ReputationScore, and VotingWeight). 3. API Design: Defined standards. 4. Architectural Review: Code review and validation of usability of the VE, level of integration, and scalability.

We propose:•Golang/WebAssembly Implementation. • Containerization (OCI compliant containers and WASM modules)• Scalability by hybrid multilayer virtualization environment (Cloud Fog and Edge) for VM serverless functions unikernels as well as container/WASM modules orchestration horizontal pod autoscaling; load balancing through Metal-Load Balancer and network configuration using a distributed service mesh and database distribution and replication/sharding. Every channel is P2P encrypted and uses advanced network protocols such as QUIC (outbound connections) and WebRTC protocols (inbound connections). •Data quality and reliability: synergistic approach including validation rules database automatic maintenance mechanisms; monitoring & alerting (Grafana Prometheus); redundancy & replication at a database level and node level as well as VE level); key-value databases for rapidly allocable caches ( Reddis) as well as a distributed P2P dynamic channel queue where the applications can share messages or subscribe to sub-channels as long as they are specified in the system VE-Relations GraphDB using Channel Adapters (runtime parsers) as well as Canonical Data Models (syntactic models) for an easy integration in the communication pipeline. • Standardized API through a synergistic approach between a GraphQL API gateway with public accessible (within the system entities) GraphQL schema and interpretable JSON/JSON-LD multidimensional models (syntactic semantic knowledge).

D2. RVWMP Technical Specifications-RVWMP is envisioned as a robust efficient and portable platform that can handle complex tasks while maintaining performance and reliability. D2 will detail all functionalities and context diagrams of the RVWMP powered by VEs.

$12,000 USD

1.System performance: Latency < 50ms for API calls; Throughput: 10,000+ requests/second; Resource utilization <70% peak load 2.Horizontal Scaling: Auto-scale from 3 to 100+ nodes within 60 seconds; Maintain performance under 5x load increase; Successful Horizontal Pod Autoscaling (HPA); Zero downtime during scaling events. 3. Technical Specification Conformance: 100% OCI container compatibility; Full WebAssembly module integration; Successful multi-layer virtualization deployment; Complete GraphQL schema implementation.

Develop the initial suite of microservices (Deep Funding Portal Interactions Voting Data Collection Blockchain Balances Explorer WaLT DID/KYC integration) and implement the standardized RESTful APIs for inter-service communication. The core RVWMP architecture supports horizontal scaling (using KVM and QUEMU - based virtualization as well as wasmedge for devices which do not support virtualization) enabling the integration of new microservices as well as vertical scaling (by applying hybrid grid computing edge virtualization and advanced virtualization techniques like host controller passthrough) to enable additional service capacity for microservices. RVWMP houses a suite of independent microservices to ensure independent data processing and logic: • Deep Funding Proposal Portal Interactions: the system manages data from proposal submissions based on interpretable models written in JSON-LD and GraphQL.• Voting Portal Data Collection: JSON and JSON-LD for data collection.• Blockchain Balances Explorer: Flipside Crypto. The configuration options within each microservice can adjust parameters and thresholds providing enhanced customization. Error handling is self-contained within each service preventing system downtime. Each microservice has the following capabilities: Self-contained data processing; Independent error handling; Configurable parameters; Horizontal (enabled by the virtualization layer) and vertical (by the hybrid multilayer) scalability.

D3. Functional microservices with API documentation and integration tests including description of the algorithms Used in Key Microservices (VEs Processing Rules): 1.Deep Funding Proposal Portal VE → Uses weighted governance scoring to calculate proposal credibility based on past funding reviewer reputation and community support trends. 2. Voting Weight Calculation VE → Uses graph-based influence propagation to assign weight dynamically factoring in historical governance impact past participation and VE verification status. 3.Trust & Sybil Resistance VE → Uses DID authentication + fraud detection models to assign governance trust factors reducing manipulation risks. 4. Scoring factors can be adjusted dynamically by the Master VE which syncs changes across its network of subordinated VEs. 5. Voting algorithms evolve based on governance model updates meaning that static preconfigured logic does not exist at the VE level besides the RE control.

$25,000 USD

1. Microservices Architecture Compliance (100% independent service encapsulation; Successful implementation of 4 core microservices; Complete isolation of data processing logic; Zero cross-service dependencies; Modular architecture conformance rate >95%). 2. Performance Metrics (Individual microservice response time <50ms; Aggregate system throughput: 5,000 requests/second; Horizontal scaling capability: 0-10 instances within 30 seconds; Vertical scaling performance improvement >40%; Resource utilization optimization <65%). 3. Virtualization and Scalability (KVM/QEMU virtualization compatibility; WasMedge integration for non-virtualized devices; Successful hybrid grid computing implementation; Edge virtualization performance optimization; Host controller passthrough technique validation). 4. API Standard Compliance (RESTful API design adherence >95%; OpenAPI/Swagger specification compliance; Standardized error response formats; API versioning and backward compatibility; Comprehensive API documentation).

The VE-Driven reputation models will be implemented under the following assumptions: 1. A RE can have multiple VEs depending on its roles in different governance domains. 2. Each VE is associated with specific governance rules that dictate its powers responsibilities and reputation mechanisms. 3. Reputation is not a single global metric; rather it is computed based on the VE’s role past behavior and governance structure. Example in the Context of Deep Funding Voting: A user (RE: Alice Smith) has two different VEs in the system: a) Alice as a Proposal Contributor (VE: Alice_ProposalContributor); b) Alice as a Voter (VE: Alice_Voter). These two VEs have different governance rules and different ways reputation is calculated. Governance rules determine what actions a VE can take and the ledger stores proof of those actions ensuring transparent auditable reputation tracking. Ex.: Proposal Contributor VE -If Alice submits a proposal and it gets approved her Proposal Contributor reputation score increases. Score+=(ProposalImpact∗ApprovalRate)−Decay ; Voter VE -If Alice votes on proposals and her past votes align with community decisions she gains a Voting Trust Score. Score+=(AlignmentFactor∗ParticipationRate)−FraudRiskPenalty .

D4. Description of Reputation Model Implementation: details on a) How Reputation is Calculated in Context (see above example); b) How Virtual Entities Control Voting Power- Voting in Deep Funding is not just token-based; it is governance-driven meaning that a voter’s weight depends on their role-based reputation. VEs dynamically determine the weight of a vote based on: 1.Past governance contributions; 2. Reputation score from past proposal reviews; c) Decentralized identity verification (DID/KYC) from trusted VEs. Ex. How VEs Adjust Voting Weight Dynamically: <Voter VE- Has voted on at least 10 proposals-Weight=BaseWeight∗EngagementMultiplier>; <Proposal Contributor VE Has had at least 2 accepted proposals Weight+=TrustFactor Expert VE (Reviewer)>; <Has been validated by governance VEs Weight+=GovernanceAuthorityFactor>. c) How Voting Rights are Verified in the Ledger (Every VE registers its authority and governance validation in the ledger. Before voting a voter VE queries the ledger to check eligibility and past behavior. The ledger ensures that Sybil attacks and vote manipulation are prevented using ZKPs. d) How Reputation Transactions Work: Each VE logs its reputation-relevant actions in the ledger. Actions that influence reputation include: Voting on proposals; Submitting or reviewing proposals; Contributing to discussions; Fraudulent activity (penalties applied). e) Reputation Adjustments Computation: ReputationScore = BaseScore+(∑EngagementFactors −DecayPenalty

$20,000 USD

- DemoComponent 1: Reputation Calculation & Contextual Influence: How reputation is computed contextually based on a VE's role and governance rules-Clear understanding of dynamic reputation scoring based on role, past actions, and governance rules. -2.DemoComponent 2: Voting Rights & Weight Verification in the Ledger: How the ledger ensures trusted voting by verifying governance roles and preventing manipulation-Proves that voting power is dynamically calculated and securely enforced using reputation scores stored on the ledger. 3. Demo Component 3: Privacy-Preserving & Auditable Reputation Adjustments: How reputation updates are fair, secure, and verifiable without exposing private data-Proves that RVWMP supports privacy-preserving, fully auditable, and role-based governance in reputation-based voting. 4.Cybersecurity tests for the demonstrator: Perform a thread model and design a penetration test for each dimension based on the analysis results (e.g.,network vulnerabilities, execution time vulnerabilities). 5.Microservice Data Outputs and Ranges: Reputation Scores (0–100) → Calculated per VE role, dynamically adjusted via governance-based weight factors; Voting Power (Relative Scaling) → Adjusted per governance role, past participation, and VE verification rules; Governance Trust Factor (0–1) → Determines Sybil resistance and trustworthiness. Proposal Impact Score (0–100) → Measures proposal acceptance, funding history, and expert reviewer endorsements.

Development of UI emphasizing intuitive microservice selection weight assignment configuration storage score visualization and blacklist functions. The UI is designed as configurable and transparent to be streamlined for intuitive use and for offering a wide range of features namely: i) Low-Code/No-Code Configuration tools; ii) Reputation Score Visualization; iii) Consent Management Dashboard; iv)Blacklist Functionality; v)Transparency & Auditability: (Tracks data sources Maintains Privacy). The UI capabilities: i) Intuitive Drag-and-Drop Interface for microservice selection and weight assignment. ii) Real-Time Score Preview enabling users to see the impact of configuration changes instantly. iii) Configuration Management enabling users to easily create modify and delete custom configurations. iv) Visualization Tools for charts graphs and data tables to display reputation scores and related metrics. v) Liquid Interface (MUIs). vi) Comprehensive Documentation (embedded help for each microfrontend) for guiding users in a personalized way through the UI and its functionalities (depending on the MUIs the help documentation will vary thus adapting to the person needs). vii) User-Friendly Configuration Interfaces for each service to modify service parameters and configurations.

D5. Working UI prototype with core functionalities user acceptance testing (UAT) plan- RVWMP provides graph-based governance-driven visualization tools ensuring clear reputation tracking while preserving privacy. The UI does not expose raw reputation data; instead it provides auditable cryptographically validated summaries. Only governance-approved reputation summaries are shown ensuring compliance with role-based access control. Ledger ensures UI visualizations remain immutable tamper-proof and auditable. Key UI Features in RVWMP: -Reputation Score Explorer → Graph-based visualization of VE reputation evolution over time. -Voting Weight Breakdown Dashboard → Real-time UI showing how governance rules impact vote weight. -Governance Role Audit Log → Displays key ledger-backed reputation decisions ensuring transparency. -Secure Data Contribution Tracker → Users can see which data sources contributed to reputation scores without revealing private interactions.

$12,000 USD

1.Usability Testing: Positive feedback from user testing. • 2. User-Customizable Scoring: The UI allows users to select microservices, assign weights, and create configurations that can be applied to various voting and decision-making instances. 3.Configuration Management.

RVWMP Resilience Framework Against Cybersecurity Attacks based on a Multi-Layered Defense Approach. In decentralized multi-agent systems various attack vectors threaten identity integrity governance decision-making reputation systems and privacy. The security resilience framework is designed to resist multiple cyber threats including: Sybil Attacks- (pose a severe risk to reputation-based voting governance decision-making and system-wide consensus mechanisms. RVWMP will integrate identity verification decentralized reputation computation and governance-driven consensus mechanisms to mitigate Sybil threats effectively.)/ Adversarial Attacks-(Attackers manipulate AI models used for reputation scoring attempting to skew voting weights or corrupt governance decisions.)/Tampering & Data Integrity Attacks-(Attackers modify governance logs alter reputation scores or manipulate reputation-weighted voting results.)/Side-Channel Attacks-(Attackers exploit system metadata timing leaks or power consumption patterns to infer confidential governance or reputation data)/Byzantine Attacks-(Malicious nodes send conflicting information to governance agents to disrupt consensus)/ Denial-of-Service (DoS) & Distributed DoS (DDoS) Attacks-(Attackers attempt to overload governance nodes by flooding them with malicious voting or reputation queries)/Data Poisoning Attacks/ Man-in-the-Middle (MitM) Attacks/ Sensor Spoofing Attacks/ Replay Attacks.

D6. Multi-layered Defense Approach with description of threat models and defense mechanisms implementation of anonymization and consent mechanisms ZKP integration roadmap. Key Defenses include: 1.Master VE-Based Identity Verification-Each Real Entity (RE) has only one Master VE preventing multiple fake identities per entity. 2.DID-Based Trust Anchors -Each VE must be cryptographically validated via DID (Decentralized Identifier) integration. 3. Graph-Based Influence Validation-RVWMP analyzes voting influence propagation to detect abnormal power concentration in governance. 4. ZKP-Protected Reputation Proofs Zero-Knowledge Proofs (ZKP) ensure reputation verification without exposing identity details. 5. Resilient Consensus Algorithm for Multi-Agent Systems-Fake identities fail to alter the consensus model as reputation weight adjustments are based on verified governance trust factors. 6. Dynamic Governance Weight Adjustments-Master VE continuously recalibrates voting weight to mitigate disproportionate influence by detecting anomalies. 7. Ledger-Anchored Governance Logs Only Master VE-approved governance actions enter the ledger preventing rogue entities from manipulating system-wide decisions.

$18,000 USD

1. Fake identities cannot gain governance power or manipulate decision-making; 2. Prevention of adversarial model corruption, ensuring voting & reputation models remain unbiased. 3. Prevention of unauthorized modification of governance records, ensuring tamper-proof reputation tracking. 4. Preventing attackers from inferring governance actions through indirect system monitoring 5. Preventing dishonest nodes from disrupting consensus, ensuring stable governance decision-making. 6.Preventing malicious users from overwhelming governance nodes, ensuring uptime and availability. 7. Preventing trust score manipulation, ensuring governance remains data-driven and reliable. 8. Preventing governance data manipulation, ensuring secure decision-making. 9. Preventing governance manipulation through falsified sensor inputs. 10. Preventing attackers from recycling governance actions, ensuring time-based integrity.

Conduct thorough testing and quality assurance deploy the RVWMP to a cloud environment and implement monitoring tools for system health and performance. Continuous review and Expansion of Virtual Entity Integration and use.

D7. Deployed RVWMP instance test results monitoring dashboards and operational documentation. D8. Expansion of Virtual Entity Integration and use.

$25,000 USD

1. New microservices can be added and integrated into the platform without disrupting existing functionality. 2. Individual microservices can be updated, scaled, or fail without causing system-wide failures or affecting other microservices. 3. Users can successfully select microservices, assign weights, create, save, and apply configurations through the UI to influence voting and decision-making. Different scoring scenarios can be easily compared. 4. The platform can handle increased load (number of users, transactions, data volume) by adding more microservices (horizontal) or increasing the capacity of individual microservices (vertical) without significant performance degradation. Response times remain within acceptable limits under peak load. 5. Microservices can communicate with each other and with external tools (knowledge graphs, databases, DID/KYC systems) seamlessly through standardized APIs (RESTful, GraphQL, etc.). Data exchange is efficient and validated against JSON Schema. 6. The platform demonstrates robust error handling and fault isolation. Individual microservice failures do not lead to system-wide downtime or data corruption. The system automatically recovers from failures. 7. All reputation score calculations and changes are transparently tracked and auditable through a graph-based system and distributed ledger. The history of reputation changes is immutable. 8. Implements privacy-preserving techniques for secure data handling & user consent.

Plan for support and maintenance after system approval.

D8. Detailed login documentation of running system

$19,000 USD

1. Support requests and responsiveness. 2. The platform maintains a high level of uptime (e.g., 99.9% availability). Scheduled maintenance windows are clearly communicated and minimized.