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Retrieving metadata, peer-reviewed manuscripts, and academic journals.
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Retrieving metadata, peer-reviewed manuscripts, and academic journals.
International Research Papers Organisation (IRPO) provides a rigorous double-blind peer-review publishing infrastructure with perpetual open access, Crossref DOI registration, and automated Google Scholar indexing.
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Benchmarked against established scholarly publishing frameworks (PKP OJS and Elsevier Editorial Manager models) with rigorous double-blind evaluation gates.
Author submits papers via structured intake wizard. Automated MIME validation, author anonymization check (double-blind sanitization), and conflict-of-interest declarations.
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Authors submit point-by-point rebuttal to reviewer comments. Upon formal acceptance notification, post-acceptance APC billing is unlocked.
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Peer-reviewed open access papers available with direct PDF downloads, Google Scholar indexing, and BibTeX citations.
James C. Corbett, Jeffrey Dean, Sanjay Ghemawat, Andrew Fikes
Spanner is Google’s scalable, multi-version, globally-distributed, and synchronously-replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This paper describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This interface and its implementation are critical to supporting external consistency and a variety of powerful features: non-blocking reads in the past, lock-free read-only transactions, and atomic schema changes, across all of Spanner.
Ana Reyna, Cristian Martín, Jaime Chen, Manuel Díaz
The Internet of Things (IoT) is a rapidly growing technology where smart devices gather and share information to offer advanced services to users. However, IoT applications present major challenges in privacy, centralized security bottlenecks, and data reliability. Blockchain technology and decentralized smart contracts provide cryptographic immutability, decentralized consensus, and automated transaction verification without a trusted third party. In this paper, we analyze the integration of blockchain and smart contracts into IoT systems, surveying existing architectures, consensus bottlenecks, transaction overheads, and practical deployment models.
Jeffrey Dean, Sanjay Ghemawat
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
Amina Adadi, Mohammed Berrada
In recent years, Artificial Intelligence (AI) has experienced unprecedented growth, becoming a driver of innovation across automated systems, healthcare diagnostics, and autonomous vehicles. However, the opacity of modern machine learning models—often referred to as "black-box" algorithms—poses severe risks to accountability, safety, and trustworthiness. Explainable Artificial Intelligence (XAI) has emerged to bridge this gap. In this survey, we provide a structured taxonomy of explanation methods, examine post-hoc interpretability vs intrinsic transparency, and highlight open challenges in evaluating AI explanations for human stakeholders.
Official scholarly journals published under IRPO editorial governance.
Premier international peer-reviewed journal dedicated to foundational and applied breakthroughs in distributed systems, software engineering, formal verification, memory safety, and cybersecurity.
Scholarly open-access publication advancing artificial intelligence theory, preference optimization, multi-agent reinforcement learning, and foundation model alignment.
International peer-reviewed journal focused on post-quantum cryptography, zero-knowledge proofs, hardware security primitives, and verifiable enclave computing.
Scholarly journal exploring sensor networks, autonomous swarm robotics, edge-AI acceleration, cyber-physical systems, and 6G industrial telemetry.
Premier open-access journal covering computational genomics, protein structure prediction, clinical decision support systems, and medical multimodal learning.
Peer-reviewed publication on carbon-aware cloud orchestration, smart microgrids, green hardware accelerators, and clean energy modeling.
IRPO adheres to international Committee on Publication Ethics (COPE) core practices. All articles undergo thorough double-blind peer assessment. IRPO maintains complete transparency and verifies all external metadata and registrations independently.