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cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Verifi-Chain: A Credentials Verifier using Blockchain and...
Tasfia Rahman, Sumaiya Islam Mouno, Arunangshu Mojumder Raatul, · 2023-07-12 · via cs.DC updates on arXiv.org

Submitting fake certificates is a common problem in Southeast Asia, which prevents qualified candidates from getting the jobs they deserve. When applying for a job, students must provide academic credentials as proof of their qualifications, acquired both inside and outside the classroom. Verifying academic documents before hiring is crucial to prevent fraud. Employing blockchain technology has the potential to address this issue. Blockchain provides an electronic certificate that is tamper-proof and non-repudiable, making it difficult for students to manipulate their academic credentials. This paper presents a prototype for an academic credential verification model that leverages the security features of blockchain and IPFS (Interplanetary File System). Certificates are temporarily stored in a database before being transferred to IPFS, where a unique hash code is generated using a hashing algorithm. This hash code serves as the certificate's unique identity and is stored in the blockchain nodes. Companies can verify an applicant's credentials by searching for the applicant and accessing their already verified certificates. Utilizing IPFS as a middleman storage platform lowers the expenses of directly storing massive data on the blockchain. To sum it up, the proposed solution would make the process of certificate verification more efficient, secure, and cost-effective. It would save time and resources that would otherwise be used to manually verify certificates.