The Indus Valley script (c. 2600-1900 BCE) is a metrological cargo-tag system, not a phonetic language. Five fields: merchant mark, commodity class, weight tier, quantity multiplier, trade route. Two Mohenjo-daro seals (M-52A, M-148A) decoded end-to-end from real CISI sign-sequence data through the Parpola-Mahadevan concordance into a metrological codebook. Both produce jar goods as commodity -- independently the most frequent sign in the Mahadevan corpus (~10% of all occurrences). M-52A produces a Mesopotamia route marker, consistent with its findspot at the primary IVC export hub. Neither result is circular. 20 morphological parallels between Tamil Nadu Iron Age potsherds and IVC seals transcribed from Rajan & Sivanantham (2025). 18 pending -- Harappa/Kalibangan/Rojdi data not yet in the open CISI corpus. Normalised SQLite corpus database: 2,373 signs from 5 sources, 179 CISI inscriptions, 397 concordance entries, 600 Tamil proxy inscriptions. Schema verified by Alloy 6: 16 assertions, all UNSAT at scope 6, zero counterexamples. Five F# scripts (dotnet fsi): codebook seeder, corpus ingestion, LSSC transition entropy analysis, IVC cargo-tag decoder, Tamil Nadu cross-corpus decoder. Codebook frozen as typed F# records generated once by SqlHydra v4 -- decoder runs with zero database dependency. CMake build system with loud dependency checks. All claims queryable with sqlite3. All computations reproducible with dotnet fsi. The Indus decode pipeline produces its result through five independent data paths: corpus frequency, concordance shape matching, findspot geography, cross-corpus overlap, and physical weight calibration. None shares a common error source. Under generous per-path bounds favouring the null hypothesis, the joint probability that all five convergences are coincidental is 3.1 x 10^-5, one chance in thirty-two thousand. The same Bernoulli independence structure that rules out universal stratigraphic disturbance across Tamil Nadu at 10^-7 rules out coincidental decode at 10^-5. The cargo-tag model is not proven. But the hypothesis that these convergences are accidental requires a one-in-thirty-two-thousand coincidence across data sources that do not talk to each other. Noise scatters. Signal clusters. The decode clusters. Indus script is accounting code operating inside a checkpoint-verified commercial network whose primary fraud-prevention mechanism is physical comparison of seal to cargo at every transit node. This paper is about the origin of incentive-compatible distributed fraud detection with accumulated reputational capital, tokenised in tamper-evident physical medium, at civilisational scale, four thousand years before the earliest known comparable system Interactive dashboard: ledger-of-meluhha.html (single file, drop indus_corpus.db to render trade network, decode seals live, filter routes by commodity). Peer review requested. Keywords: Indus Valley, Harappan script, metrological accounting, Bronze Age trade, Meluhha, CISI, Alloy, SQLite, F#, SqlHydra, cargo tag, Tamil Nadu, Rajan-Sivanantham, formal verification License: CC BY 4.0 Upload files:1. ledger_of_meluhha.pdf (21 pages)2. ledger_of_meluhha_overleaf.zip (tex + citations.lua -- set compiler to LuaLaTeX)3. ledger_of_meluhha_dashboard.zip (html + corpus db)
Omer Aziz, Muhammad Shoaib Farooq, Junaid Nasir Qureshi, Muhammad Faraz Manzoor · 5 authors
(1) Background: A blockchain-based framework for distributed agile Open-Source Software for Archaeological Photogrammetry (OSSAP) testing life cycle is an innovative approach that uses blockchain technology to optimize the Open-Source Software for Archaeological Photogrammetry process. Previously, various methods have been employed to address communication and collaboration challenges in Open-Source Software for Archaeological Photogrammetry, but they were inadequate in aspects such as trust, traceability, and security. Additionally, a significant cause of project failure was the non-completion of unit testing by developers, leading to delayed testing. (2) Methods: This article discusses the integration of blockchain technology in Open-Source Software for Archaeological Photogrammetry and resolves critical concerns related to transparency, trust, coordination, testing and communication. A novel approach is proposed based on a blockchain framework named Open-Source Software for Archaeological Photogrammetry Testing-Plus. (3) Results: The Open-Source Software for Archaeological Photogrammetry Testing-Plus framework utilizes blockchain technology to provide a secure and transparent platform for acceptance testing and payment verification. Moreover, by leveraging smart contracts on a private Ethereum blockchain, Open-Source Software for Archaeological Photogrammetry Testing-Plus ensures that both the testing team and the development team are working towards a common goal and are compensated fairly for their contributions. (4) Conclusions: The experimental results conclusively show that this innovative approach substantially improves transparency, trust, coordination, testing and communication and provides security for both the testing team and the development team engaged in the distributed agile Open-Source Software for Archaeological Photogrammetry (Open-Source Software for Archaeological Photogrammetry) testing life cycle.
The performance of cryptocurrency stocks is essential in shaping the growth of digital assets, which have gained significant popularity in the modern era as they offer various benefits to the public, such as reduced fees and hassle-free staking. Therefore, it’s essential for companies to focus on key factors like durability, accessibility, speed, regulation, and efficient management. More investigation is necessary to truly unlock the potential of virtual staking. In order to address this question and conduct a comprehensive and insightful analysis of optimal digital equity, this study presents a ground-breaking, unified approach of entropy- Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) approach implemented on comprehensive systems of picture fuzzy rough numbers. Picture fuzzy rough entropy technique is executed for the purpose of analyzing the worth of each crypto stake. Then, a picture fuzzy rough-MARCOS method is carried out for the alternatives prioritizing purposes. The estimation mechanism of digital shares involves the analysis of several famous crypto exchanges active in Türkiye depending upon seven crucial elements affecting the capability of each alternative. Further, the sensitivity and comparative analysis are performed for the purpose of assuring the versatility and durability of the proposed method. The shareholders may be able to follow the path built by the discoveries of the research in order to take suitable manoeuvres without enduring steep expenses.
Verifying image provenance has become an important topic, especially in the realm of news media. To address this issue, the Coalition for Content Provenance and Authenticity (C2PA) developed a standard to verify image provenance that relies on digital signatures produced by cameras. However, photos are usually edited before being published, and a signature on an original photo cannot be verified given only the published edited image. In this work, we describe VerITAS, a system that uses zero-knowledge proofs (zk-SNARKs) to prove that only certain edits have been applied to a signed photo. While past work has created image editing proofs for photos, VerITAS is the first to do so for realistically large images (30 megapixels). Our key innovation enabling this leap is the design of a new proof system that enables proving knowledge of a valid signature on a large amount of witness data. We run experiments on realistically large images that are more than an order of magnitude larger than those tested in prior work. In the case of a computationally weak signer, such as a camera, we are able to generate a proof of valid edits for a 90 MB image in just over thirteen minutes, costing about $0.54 on AWS per image. In the case of a more powerful signer, we are able to generate a proof of valid edits for a 90 MB image in just over three minutes, costing only $0.13 on AWS per image. Either way, proof verification time is less than a second. Our techniques apply broadly whenever there is a need to prove that an efficient transformation was applied correctly to a large amount of signed private data.
Himanshu Tiwari, Ayush Raj, Ujjwal Kr. Singh, Hoor Fatima
Incorporating generative artificial intelligence (AI) into design and art has upended established creative paradigms, sparking discussions on the validity of AI-generated art and the development of non-fungible token (NFT) marketplaces. The US Copyright Office rendered a significant decision in February 2023 that highlights the contentious nature of AI work and the need of human intervention in its commercialization. This paper traces the development of artificial intelligence in neural networks and examines how it has affected visual arts. We investigate the idea of autonomously creating digital art in the NFT style utilizing generative adversarial networks (GANs), with striking results. Our work links deep learning and blockchain, enabling AI to find a place in the digital art market.
Generative Adversarial Networks and Image Synthesis
Kateryna Dmytrivna Yanishevska, Дмитро Мурач, Аліна Гончарова
The article examines the impact of the latest technologies, in particular NFTs (non-fungible tokens), on the art market and the protection of intellectual property rights in the context of digital transformation.The authors analyse the dynamics of the art market, pointing out its rapid growth, as well as the main trends, including the introduction of blockchain technologies and NFTs, which signifi cantly change traditional approaches to the valuation and protection of art objects.At the same time, the author emphasises the importance of improving legislation, in particular, regarding the legal status of NFT and its application in the context of copyright protection.The article discusses the existing problems and challenges, including the growth of piracy in the digital environment, and suggests solutions, including through the use of a blockchain platform to confi rm authorship and protect rights to digital works of art.The authors also focus on the low legal culture in cyberspace, which increases the number of copyright infringements, and the rigidity of the legal system, which cannot quickly adapt to changes in technological progress, in particular in the fi eld of digital technologies and artifi cial intelligence.Another problem is that it is very diffi cult to prove the authorship of a work within the framework of legal procedures, in particular in cyberspace.Thus, the article highlights the need to reform the legal system for more effective protection of intellectual property rights.
Yifan Chen, Lei Li, Xinyu Hu, Jiahao Li · 6 authors
The use of Artificial Intelligence (AI) generators to create digital artwork as the content of Non-Fungible Tokens (NFTs) is prevalent. Typically, when minting AI-generated digital artwork into NFTs, the data of digital artwork is stored in the cloud or decentralized storage system, and a Uniform Resource Identifier (URI) or Content Identifier (CID) of the data is stored in the smart contract of NFTs to access the data. This makes AI art NFTs suffer from potential asset loss as conventional NFTs. Can AI be utilized to enhance the availability of AI-generated digital assets as NFT content? In this paper, we propose a new method for minting AI-generated digital assets into NFTs. The key idea of our approach is to store the latent codes of the generated assets on the blockchain instead of URI or CID in conventional NFTs. Here, the latent codes are intermediate variables in the process of generating digital assets by the generator and could restore the assets through the generator. Meanwhile, to be able to restore assets, the universal generator is stored on a distributed system, and its high popularity guarantees its availability. Experiments demonstrate the feasibility of our method. In addition, the integrity and the availability of assets minted by the proposed method and the existing ones are discussed, concluding that our approach has better availability while safeguarding integrity.
Generative Adversarial Networks and Image Synthesis
Kar Balan, Shruti Agarwal, Simon Jenni, Andy Parsons · 6 authors
We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance -- determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI.
Open access
3 source records
Generative Adversarial Networks and Image Synthesis
Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for digital art is growing aggressively. In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training. We compare various architectures and designs of DC-GANs to arrive at a recommendable design choice for a stable and realistic generation. The main focus of the work is to generate realistic images that do not exist in reality but are synthesised from random noise by the proposed model. We provide visual results of generated animal face images (some pieces of evidence showing a blend of species) along with recommendations for training, architecture and design choices. We also show how training image preprocessing plays a massive role in GAN training.
Open access
2 source records
Generative Adversarial Networks and Image Synthesis