A novel data structure that enables the storage and retrieval of linear array numeric data with logarithmic time complexity updates, range sums, and rescaling is introduced and studied. Computing sums of ranges of arrays of numbers is a common computational problem encountered in data compression, coding, machine learning, computational vision, and finance, among other fields. Efficient data structures enabling log n updates of the underlying data (including range updates), queries of sums over ranges, and searches for ranges with a given sum have been extensively studied (n being the length of the array). Two solutions to this problem are well-known: Fenwick trees (also known as Binary Indexed Trees) and Segment Trees. The new data structure extends the capabilities for the first time to further enable multiplying (rescaling) ranges of the underlying data by a scalar as well in log n. Scaling by 0 can be enabled, with the effect that subsequent updates may take (log n)2time. The new data structure introduced here consists of a pair of interacting Fenwick tree-like structures, one of which holds the unscaled values and the other of which holds the scalars. Experimental results demonstrating performance improvements for the multiplication operation on arrays from a few dozen to over 30 million data points are discussed. This research was done as part of Ajna Labs in the course of developing a decentralized finance protocol. It enables an efficient on-chain encoding and processing of an order book-like data structure used to manage lending, interest, and collateral.
Cryptocurrencies, introduced in 2009 with the first cryptocurrency, Bitcoin, have grown significantly in recent years and attracted attention globally. One of the main characteristics of cryptocurrencies and their key innovation is that they are underpinned by distributed ledger technology (DLT) or blockchain as a type of DLT. This technology enables cryptocurrencies to be transferred, stored or traded electronically within DLT-based systems in a peer-to-peer manner among (pseudonymous) system participants across the world without the involvement of the usual central trusted authorities or intermediaries such as banks. This raises the question of if, and how, one should ascertain internationality for cryptocurrency transfers taking place within truly global systems underpinned by DLT for private international law purposes. This article aims to raise awareness of and address the question of internationality in the context of cryptocurrency transfers in DLT-based systems. It considers internationality in private international law, potential factors that might be relevant in ascertaining internationality for cryptocurrency transfers through a comparison to that for electronic funds transfers (EFTs), and the approaches of the International Institute for the Unification of Private Law (UNIDROIT) and the Hague Conference for Private International Law (HCCH) on internationality in their current projects concerning digital assets and digital economy respectively.
The music industry has undergone a profound transformation in the digital age, shifting from traditional physical media to digital formats and streaming services. This article provides an overview of this evolution, emphasizing key milestones and trends. It begins by exploring the dominance of physical media, such as vinyl records, cassette tapes, and CDs, and the impact of these formats on music distribution and consumption. The digital revolution is then examined, highlighting the emergence of digital music formats like MP3 and AAC, along with pioneering services like Napster and iTunes that facilitated digital music consumption. The subsequent section discusses the rise of streaming services like Spotify and Apple Music, which have redefined how music is accessed and monetized. In the current landscape, several trends are shaping the music industry, including the integration of artificial intelligence (AI) for music recommendation and composition, blockchain technology for transparent royalty tracking, and the use of non-fungible tokens (NFTs) to tokenize music ownership. Finally, the article speculates on the future of the music industry in the digital era, considering possibilities such as virtual reality (VR) and augmented reality (AR) experiences for live performances and the evolving role of social media in music trends and fan engagement. As the music industry continues to adapt to technological advancements and changing consumer preferences, it faces both opportunities and challenges. Navigating these shifts requires collaboration, innovation, and a commitment to fairly compensating artists. The journey of the music industry through the digital age is a dynamic narrative that will continue to unfold in fascinating ways.
Chryssis Georgiou, Michel Raynal, Elad M. Schiller
Numerous distributed applications, such as cloud computing and distributed ledgers, necessitate the system to invoke asynchronous consensus objects an unbounded number of times, where the completion of one consensus instance is followed by the invocation of another. With only a constant number of objects available, object reuse becomes vital. We investigate the challenge of object recycling in the presence of Byzantine processes, which can deviate from the algorithm code in any manner. Our solution must also be self-stabilizing, as it is a powerful notion of fault tolerance. Self-stabilizing systems can recover automatically after the occurrence of arbitrary transient faults, in addition to tolerating communication and (Byzantine or crash) process failures, provided the algorithm code remains intact. We provide a recycling mechanism for asynchronous objects that enables their reuse once their task has ended, and all non-faulty processes have retrieved the decided values. This mechanism relies on synchrony assumptions and builds on a new self-stabilizing Byzantine-tolerant synchronous multivalued consensus algorithm, along with a novel composition of existing techniques.
Ethereum is one of the first blockchains executing smart contracts (financial applications directly executed on the ledger using a virtual machine). The performance of the Ethereum virtual machine has become a bottleneck caused by high volumes of transactions due to blockchain applications, including decentralized finance and non-fungible tokens. Hence, there is a need to detail the execution characteristics of the Ethereum virtual machine before its performance can be improved. This work introduces an off-line Ethereum virtual machine tracer called EVMTracer that produces runtime data dependence graphs from executed transactions as an alternative program representation. From the runtime dependence graphs, we can deduce valuable metrics about program execution characteristics, including the degree of parallelization and computational redundancies in smart contracts. Our experiments encompass all blocks up to 12M on the Ethereum mainnet. We found a geometric mean of 1.90× theoretical maximum speedup when executing the smart contracts in parallel and identified 34.97% of SLOAD instructions as redundant.
Franck Cassez, Joanne Fuller, Milad K. Ghale, David J. Pearce · 5 authors
The Ethereum protocol implements a replicated state machine. The network participants keep track of the system state by: 1) agreeing on the sequence of transactions to be processed and 2) computing the state transitions that correspond to the sequence of transactions. Ethereum transactions are programs, called smart contracts, and computing a state transition requires executing some code. The Ethereum Virtual Machine (EVM) provides this capability and can execute programs written in EVM bytecode. We present a formal and executable semantics of the EVM written in the verification-friendly language Dafny: it provides (i) a readable, formal and verified specification of the semantics of the EVM; (ii) a framework to formally reason about bytecode.
Cryptocurrencies have taken the finance world by storm and emerged as global payment tokens in the emerging area of decentralized finance and also as investment avenues of great interest among investors, especially during and post-Covid times.Cryptocurrency or a crypto is a collection of binary data which is designed to work as a medium of exchange. However, today cryptocurrency has taken many forms and derived different utilities. With cryptocurrency being at the centre of decentralised financing, various forms of new / modern decentralised financing avenues have opened up. A cryptocurrency can be used as a form of digital currency, as cash to pay for the purchase of everyday items, to purchase of large items such as buildings, cars etc. It can be bought through several digital wallets or through trading platforms like WazirX, CoinSwitch, CoinDCX, etc., Meanwhile, cryptocurrencies are treated as property or investment avenues as well in several countries. In India, mostly crypto is treated as an investment avenue similar in functionality that of a share.Though crypto is a buzz-word in finance world these days, there are a lot of uncertainties involved with it when it comes to its regulation, taxation and volatility, etc. Thus, in this paper an attempt is made to understand the factors which influences the investors to invest in cryptocurrencies.
User identification in decentralized systems is a demanding task. Identification systems should work resiliently and have efficient performance. Moreover, identification systems should protect the data that they must store against hackers and saboteurs. Keeping a system with decentralized identification without any intervention in the middle has attracted attention to improve earlier centralized identification systems. Decentralized Identifiers (DIDs) constitute a solution for identification divided into different modules. The verifiable data registry is one of the main parts of this technology, which is distributed storage of identity properties. We analyze the decentralized identification data registry and compare the performance of verifiable data registry based on blockchain and the Distributed Hash Table (DHT) on different scales of systems. Our evaluation results show that DHT has better performance. Furthermore, a model based on DHT shows that in addition to immutable storage and faster query time, it makes systems handle or search in data storage with lower searching time compared to Ethereum Blockchain as another immutable secure technology. Finally, our results show that DHT is a better solution than other models in different scenarios. Although blockchain has promising results on a small scale, it still has problems with storage and query time in large-scale systems.
Web3 aims at creating a decentralized platform that is competitive with modern cloud infrastructures that support today's Internet. However, Web3 is still limited, supporting only applications in the domains of content creation and sharing, decentralized financing, and decentralized communication. This is mainly due to the technologies supporting Web3: blockchain, IPFS, and libp2p, that although provide a good collection of tools to develop Web3 applications, are still limited in terms of design and performance. This motivates the need to better understand these technologies as to enable novel optimizations that can push Web3 to its full potential. Unfortunately, understanding the current behavior of a fully decentralized large-scale distributed system is a difficult task, as there is no centralized authority that has full knowledge of the system operation. To this end, in this paper we characterize the workload of IPFS, a key enabler of Web3. To achieve this, we have collected traces from accesses performed by users to one of the most popular IPFS gateways located in North America for a period of two weeks. Through the fine analysis of these traces, we gathered the amount of requests to the system, and found the providers of the requested content. With this data, we characterize both the popularity of requested and provided content, as well as their geo-location (by matching IP address with the MaxMind database). Our results show that most of the requests in IPFS are only to a few different content, that is provided by large portion of peers in the system. Furthermore, our analysis also shows that most requests are provided by the two largest portions of providers in the system, located in North America and Europe. With these insights, we conclude that the current IPFS architecture is sub-optimal and propose a research agenda for the future.
Zk-SNARK unleashes the great potential of ZKP (zero-knowledge proof) in the blockchain, distributed storage, etc. However, the proof-generation of zk-SNARK is excessively time intensive, making it a challenge to deploy a high-performance zk-SNARK in most real applications. As a result, NTT (Number Theoretic Transform), one of the most time-consuming parts in proof-generation, needs to be accelerated significantly. To address this issue, we propose a novel and efficient “data reordering” technique to enable a highly pipelined architecture, on which an FPGA-based hardware accelerator is designed to support the large-bitwidth and large-scale NTT tasks in zk-SNARK. Our architecture achieves a two-level pipeline: 1) the top-level pipeline is achieved among smaller NTT sub-tasks, which are decomposed from a large-scale NTT task; 2) the bottom-level pipeline is achieved in each sub-task, among butterfly operations with different step sizes. This architecture can effectively reduce the data dependency and memory access requirements, meanwhile, can be flexibly scaled to different scales of FPGAs. To balance computing efficiency and flexibility, the OpenCL equipped with HLS is used to implement the heterogeneous acceleration system. We prototype the accelerator on the AMD-Xilinx Alveo U50 card (UltraScale+ XCU50 FPGA). The evaluation results show that 1) our accelerator shows high scalability for different scales of FPGAs with a stable performance improvement; 2) it performs 1.95× faster than the one in PipeZK; 3) and it achieves 27.98×, 1.74× speedup and 6.9×, 6× energy efficiency improvement than AMD Ryzen 9 5900X single core and 12 cores respectively when integrated into the well-known ZKP open-source project, Bellman.
Private blockchain as a replicated transactional system shares many commonalities with distributed database. However, the intimacy between private blockchain and deterministic database has never been studied. In essence, private blockchain and deterministic database both ensure replica consistency by determinism. In this paper, we present a comprehensive analysis to uncover the connections between private blockchain and deterministic database. While private blockchains have started to pursue deterministic transaction executions recently, deterministic databases have already studied deterministic concurrency control protocols for almost a decade. This motivates us to propose Harmony, a novel deterministic concurrency control protocol designed for blockchain use. We use Harmony to build a new relational blockchain, namely HarmonyBC, which features low abort rates, hotspot resiliency, and inter-block parallelism, all of which are especially important to disk-oriented blockchain. Empirical results on Smallbank, YCSB, and TPC-C show that HarmonyBC offers 2.0x to 3.5x throughput better than the state-of-the-art private blockchains.
Our work presents two fundamental contributions. On the application side, we tackle the challenging problem of predicting day-ahead crypto-currency prices. On the methodological side, a new dynamical modeling approach is proposed. Our approach keeps the probabilistic formulation of the state-space model, which provides uncertainty quantification on the estimates, and the function approximation ability of deep neural networks. We call the proposed approach the deep state-space model. The experiments are carried out on established cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been to predict the price for the next day. Benchmarking has been done with both state-of-the-art and classical dynamical modeling techniques. Results show that the proposed approach yields the best overall results in terms of accuracy.
Since executing a smart contract on the Ethereum blockchain costs money (measured in gas ), smart contract developers spend significant effort in reducing gas usage. In this paper, we propose a new technique for reducing the gas usage of smart contracts by changing the underlying data layout. Given a smart contract P and a type-level transformation, our method automatically synthesizes a new contract P ′ that is functionally equivalent to P . Our approach provides a convenient DSL for expressing data type refactorings and employs program synthesis to generate the new version of the contract. We have implemented our approach in a tool called Solidare and demonstrate its capabilities on real-world smart contracts from Etherscan and GasStation. In particular, we show that our approach is effective at automating the desired data layout transformation and that it is useful for reducing gas usage of smart contracts that use rich data structures.
Smart contracts are pieces of code that reside inside the blockchains and can be triggered to execute any transaction when specifically predefined conditions are satisfied. Being commonly used for commercial transactions in blockchain makes the security of smart contracts particularly important. Over the last few years, we have seen a great deal of academic and practical interest in detecting and fixing the bugs in smart contracts written by Solidity. But little is known about the real bug fixes in Solidity smart contract projects. To understand the bug fixes and enrich the knowledge of bug fixes in real-world projects, we conduct an empirical study on historical bug fixes from 46 real-world Solidity smart contract projects in this paper. We provide a multi-faceted discussion and mainly explore the following four questions: File Type and Amount, Fix Complexity, Bug distribution, and Fix Patches. We distill four findings during the process to explore these four questions. Finally, based on these findings, we provide actionable implications to improve the current approaches to fixing bugs in Solidity smart contracts from three aspects: Automatic repair techniques, Analysis tools, and Solidity developers.
Leonhard Balduf, Martin Florian, Björn Scheuermann
We explore issues relating to the storage of digital art, based on an empirical investigation into the storage of audiovisual data referenced by non-fungible tokens (NFTs). We identify current trends in NFT data storage and highlight problems with implemented solutions. We particularly focus our investigation on the use of the Interplanetary Filesystem (IPFS), which emerges as a popular and versatile distributed storage solution for NFTs. Based on the analysis of discovered data storage techniques, we propose a set of best practices to ensure long-term storage survivability of NFT data. While helpful for forming the NFT art market into a legitimate long-term environment for digital art, our recommendations are also directly applicable for improving the availability and integrity of non-NFT digital art.
Open access
2 source records
Advanced Data Storage Technologies
Digital and Traditional Archives Management
Advanced Steganography and Watermarking Techniques
The growth in information and communication technology has led to phenomenons in the financial sector as well. This primarily alludes to the introduction of cryptocurrencies, a decentralised medium of exchange, which provides an alternative to the centuries-old idea of physical money. There is a visible relationship between the principles of behavioural finance and the value/returns that these cryptocurrencies have. These currencies are not dependent on the behaviour of the financial markets and economy but instead on the supply and demand of the currency along with its popularity which is dependent purely on the individuals.
A fundamental issue in blockchain systems is their scalability in terms of data storage, computation, communication, and security. To resolve this issue, a promising research direction is coding theory, which is widely used for distributed storage, recovery from erasures or channel errors and/or to reduce communication cost. To this end, this article provides the first comprehensive survey of approaches that employ coding theory to scale blockchain systems. It shows how the use of coded symbols or shards allow participants to only store a fraction of the total blockchain, protect against malicious nodes or erasures, ensure data availability in order to promote transparency, and scale the security of sharded blockchains. Further, coded symbols help reduce communication cost when disseminating blocks, which help bootstrap new nodes and speed up consensus of blocks. For each category of solutions, we highlight problems and issues that motivated their designs and use of coding. Moreover, we provide a qualitative analysis of their storage, communication, and computation costs.
Recent years have witnessed growing consolidation of web operations. For example, the majority of web traffic now originates from a few organizations, and even micro-websites often choose to host on large pre-existing cloud infrastructures. In response to this, the "Decentralized Web" attempts to distribute ownership and operation of web services more evenly. This paper describes the design and implementation of the largest and most widely used Decentralized Web platform --- the InterPlanetary File System (IPFS) --- an open-source, content-addressable peer-to-peer network that provides distributed data storage and delivery. IPFS has millions of daily content retrievals and already underpins dozens of third-party applications. This paper evaluates the performance of IPFS by introducing a set of measurement methodologies that allow us to uncover the characteristics of peers in the IPFS network. We reveal presence in more than 2700 Autonomous Systems and 152 countries, the majority of which operate outside large central cloud providers like Amazon or Azure. We further evaluate IPFS performance, showing that both publication and retrieval delays are acceptable for a wide range of use cases. Finally, we share our datasets, experiences and lessons learned.
I’m going to talk from an artist’s perspective about “Art and NFTs—Past and Future.” There are a lot of surprising details in the short history of non-fungible tokens (NFTs) and some pretty interesting ideas that are ready to unfold in the future. As an artist, the work I’ve done has always been media-based, including video, software, and related forms. Not so long ago, I was making video artworks, akin to short, experimental, independent films. Along with my partner, Jennifer McCoy, I have produced “net art”—art made for viewing and audience participation on the Internet. Since all of the work that I—along with my friends and other artists in the community—made was digital and intangible, there was very little way to participate in the art market. There were no tangible works that could be made and sold. A digital media-based artwork could circulate in non-commercial contexts such as art or film festivals or museum curations, but rarely could it participate in the traditional art market like a painting could. For example, in 2001, the Whitney Museum of American Art acquired a JavaScript-based project of ours called 201—A Text Algorithm. A code-based piece can enter the museum, but it is usually through donations and commissions rather than sales. That was the experience for my friends and me for a long time. Working with an intangible form are always on the sidelines of the greater visual arts community. As a result, I—along with many other artists—adopted a strategy of physicalization: you make your work sculptural and turn your media ideas into objects. We made physical media sculptures that were met with success. In 2001, the Metropolitan Museum of Art purchased an early work of ours Every Shot, Every Episode, which was recently part of the exhibition Pictures Revisited. Although Every Shot, Every Episode is a media art piece, it is exhibited sculpturally in the form of a small, wall-mounted suitcase. In other projects, we created sculptures that included video and kinetics, such as miniature film sets made with small cameras. One of these is in the Museum of Modern Art (MoMA) collection and another was purchased by the Grand Duke Jean Museum of Modern Art (MUDAM) in Luxembourg. Although the projects contain software and media, their embodiment as physical objects allows them to be displayed and collected in traditional ways. This was the course of my practice in the early 2000s. To be sure, I used the transition from digital to physical not only as a way to participate in the art market but also for artistic and aesthetic reasons. This choice certainly allowed my works to be collected and, as a result, a broader conversation about who was buying and supporting new media art began. It was an exciting time. But there lingered a real question about how one might make and sell work that is natively digital.
Blockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in Ethereum Virtual Machine (EVM), making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, which price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the Ethereum Virtual Machine. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up comparing to StringUtils (a popular Solidity string library).
Haoxian Chen, Gerald Whitters, Mohammad Javad Amiri, Yuepeng Wang · 5 authors
This paper presents DeCon, a declarative programming language for implementing smart contracts and specifying contract-level properties. Driven by the observation that smart contract operations and contract-level properties can be naturally expressed as relational constraints, DeCon models each smart contract as a set of relational tables that store transaction records. This relational representation of smart contracts enables convenient specification of contract properties, facilitates run-time monitoring of potential property violations, and brings clarity to contract debugging via data provenance. Specifically, a DeCon program consists of a set of declarative rules and violation query rules over the relational representation, describing the smart contract implementation and contract-level properties, respectively. We have developed a tool that can compile DeCon programs into executable Solidity programs, with instrumentation for run-time property monitoring. Our case studies demonstrate that DeCon can implement realistic smart contracts such as ERC20 and ERC721 digital tokens. Our evaluation results reveal the marginal overhead of DeCon compared to the open-source reference implementation, incurring 14% median gas overhead for execution, and another 16% median gas overhead for run-time verification.