Exploring the data of blockchain-based metaverses

In recent years the concept of metaverse has evolved in the attempt of defining richer immersive and interactive environments supporting various types of virtual experiences and interactions among users. This has led to the emergence of various different metaverse platforms that utilize blockchain technology and non-fungible tokens (NFTs) to establish ownership of metaverse elements and attach features and information to it. This article will delve into the heterogeneity of the data involved in these metaverse platforms, as well as highlight some dynamics and features of them. Moreover, the paper introduces a metaverse analysis tool developed by the authors, which leverages machine learning techniques to collect and analyze daily data, including blockchain transactions, platform-specific metadata, and social media trends. Experimental results are reported are presented with a use-case scenario focused on the trading of digital parcels, commonly referred to as metaverse real estate.Comment: In Proceedings of the IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE METACOM 2023), June 26--28, 2023, Japa

Execution Trace Graph Based Multi-criteria Partitioning of Stream Programs

AbstractOne of the problems proven to be NP-hard in the field of many-core architectures is the Partitioning of stream programs. In order to maximize the execution parallelism and obtain the maximal data throughput for a streaming application it is essential to find an appropriate actors assignment. The paper proposes a novel approach for finding a close-to-optimal partitioning configuration which is based on the execution trace graph of a dataflow network and its anal- ysis. We present some aspects of dataflow programming that make the partitioning problem different in this paradigm and build the heuristic methodology on them. Our optimization cri- teria include: balancing the total processing workload with regards to data dependencies, actors idle time minimization and reduction of data exchanges between processing units. Finally, we validate our approach with experimental results for a video decoder design case and compare them with some state-of-the-art solutions

The impact of NFT profile pictures within social network communities

This paper presents an analysis of the role of social media, specifically Twitter, in the context of non-fungible tokens, better known as NFTs. Such emerging technology framing the creation and exchange of digital object, started years ago with early projects such as "CryptoPunks" and since early 2021, has received an increasing interest by a community of people creating, buying, selling NFT's and by the media reporting to the general public. In this work it is shown how the landscape of one class of projects, specifically those used as social media profile pictures, has become mainstream with leading projects such as "Bored Ape Yacht Club", "Cool Cats" and "Doodles". This work illustrates how heterogeneous data was collected from the Ethereum blockchain and Twitter and then analysed using algorithms and state-of-art metrics related to graphs. The initial results show that from a social network perspective, the collections of most popular NFTs can be considered as a single community around NFTs. Thus, while each project has its own value and volume of exchange, on a social level all of them are primarily influenced by the evolution of values and trades of "Bored Ape Yacht Club" collection.Comment: In Proceedings of the ACM International Conference on Information Technology for Social Good (GoodIT'22), September 07--09, 2022, Cypru

The impact of NFT profile pictures within social network communities

This paper presents an analysis of the role of social media, specifically Twitter, in the context of non-fungible tokens, better known as NFTs. Such emerging technology framing the creation and exchange of digital object, started years ago with early projects such as ”CryptoPunks” and since early 2021, has received an increasing interest by a community of people creating, buying, selling NFTs and by the media reporting to the general public. In this work it is shown how the landscape of one class of projects, specifically those used as social media profile pictures, has become mainstream with leading projects such as ”Bored Ape Yacht Club”, ”Cool Cats” and ”Doodles”. This work illustrates how heterogeneous data was collected from the Ethereum blockchain and Twitter and then analysed using algorithms and state-of-art metrics related to graphs. The initial results show that from a social network perspective, the collections of most popular NFTs can be considered as a single community around NFTs. Thus, while each project has its own value and volume of exchange, on a social level all of them are primarily influenced by the evolution of values and trades of ”Bored Ape Yacht Club” collection

Dataflow Program Analysis and Refactoring Techniques for Design Space Exploration: MPEG-4 AVC/H.264 Decoder Implementation Case Study

This paper presents a methodology to perform design space exploration of complex signal processing systems implemented using the CAL dataflow language. In the course of space exploration, critical path in dataflow programs is first presented, and then analyzed using a new strategy for computational load reduction. These techniques, together with detecting design bottlenecks, point to the most efficient optimization directions in a complex network. Following these analysis, several new refactoring techniques are introduced and applied on the dataflow program in order to obtain feasible design points in the exploration space. For a MPEG-4 AVC/H.264 decoder software and hardware implementation, the multi-dimensional space can be explored effectively for throughput, resource, and frequency, with real-time decoding range from QCIF to HD resolutions

Design space exploration strategies for FPGA implementation of signal processing systems using CAL dataflow program

This paper presents some strategies for design space exploration of FPGA-based signal processing systems that are specified using the CAL dataflow language. The actor- oriented, high-level of abstraction provided by CAL allows flexible exploration and consequently results in a wide range of feasible design implementations. We have applied and ex- tended the existing techniques for refactoring and pipelining actors and actions by means of critical path analysis, and in- troduced some new buffering techniques based on heuristics. The combinations of these techniques have been applied on the CAL specification of the MPEG-4 video decoder, and synthesized to HDL for evaluation in the design implementa- tion space. Results show that using our configuration for the exploration of 48 design points, a throughput range of roughly 8x has been achieved, for slice, block RAM, frequency, and latency range of 1.3x, 2.5x, 2.5x, and 2.9x respectively

Execution Trace Graph of Dataflow Process Networks

The paper introduces and specifies a formalism that provides complete representations of dataflow process network (DPN) program executions, by means of directed acyclic graphs. Such graphs, also known as execution trace graphs (ETG), are composed of nodes representing each action firing and by directed arcs representing the dataflow program execution constraints between two action firings. Action firings are atomic operations that encompass the algorithmic part of the action executions applied to both, the input data and the actor state variables. The paper describes how an ETG can be effectively derived from a dataflow program, specifies the type of dependencies that need to be included, and the processing that need to be applied so that an ETG become capable of representing all the admissible trajectories that dynamic dataflow programs can execute. The paper also describes how some characteristics of the ETG, related to specific implementations of the dataflow program, can be evaluated by means of high-level and architecture-independent executions of the program. Furthermore, some examples are provided showing how the analysis of the ETGs can support efficient explorations, reductions, and optimizations of the design space, providing results in terms of design alternatives, without requiring any partial implementation or reduction of the expressiveness of the original DPN dataflow program

A Multidisciplinary Approach for Model Predictive Control Education: A Lego Mindstorms NXT-based Framework

This work introduces an educational framework based on the Lego Mindstorms NXT robotic platform used to outline both the theoretical and practical aspects of the Model Predictive Control (MPC) theory. The framework has been developed in the widely used MatLab/Simulink environment. A two-wheeled inverted pendulum is considered as hands-on experimental scenario. For such a system, starting from its mathematical modeling, an established design methodology is presented aiming to outline step-by-step the predictive controller implementation on a low power architecture. This methodology stress the design of a non-linear MPC controller on a low power embedded system, pruning the designer to deal with hard real time constraints without impacting the overall design requirements. The effectiveness of this multidisciplinary approach is shown through this presentation and demonstrated with experimental results