惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - Franky
T
Tailwind CSS Blog
Microsoft Azure Blog
Microsoft Azure Blog
The Cloudflare Blog
博客园 - 叶小钗
N
Netflix TechBlog - Medium
罗磊的独立博客
量子位
MyScale Blog
MyScale Blog
A
About on SuperTechFans
Blog — PlanetScale
Blog — PlanetScale
V
Visual Studio Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
GbyAI
GbyAI
B
Blog
腾讯CDC
爱范儿
爱范儿
Recent Announcements
Recent Announcements
有赞技术团队
有赞技术团队
F
Fortinet All Blogs
雷峰网
雷峰网
G
Google Developers Blog
Google DeepMind News
Google DeepMind News

cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
PrettiSmart: Visual Interpretation of Smart Contracts via...
Xiaolin Wen, Tai D. Nguyen, Lun Zhang, Jun Sun, Yong Wang · 2024-12-24 · via cs.HC updates on arXiv.org

Smart contracts are the fundamental components of blockchain technology. They are programs to determine cryptocurrency transactions, and are irreversible once deployed, making it crucial for cryptocurrency investors to understand the cryptocurrency transaction behaviors of smart contracts comprehensively. However, it is a challenging (if not impossible) task for investors, as they do not necessarily have a programming background to check the complex source code. Even for investors with certain programming skills, inferring all the potential behaviors from the code alone is still difficult, since the actual behaviors can be different when different investors are involved. To address this challenge, we propose PrettiSmart, a novel visualization approach via execution simulation to achieve intuitive and reliable visual interpretation of smart contracts. Specifically, we develop a simulator to comprehensively capture most of the possible real-world smart contract behaviors, involving multiple investors and various smart contract functions. Then, we present PrettiSmart to intuitively visualize the simulation results of a smart contract, which consists of two modules: The Simulation Overview Module is a barcode-based design, providing a visual summary for each simulation, and the Simulation Detail Module is an augmented sequential design to display the cryptocurrency transaction details in each simulation, such as function call sequences, cryptocurrency flows, and state variable changes. It can allow investors to intuitively inspect and understand how a smart contract will work. We evaluate PrettiSmart through two case studies and in-depth user interviews with 12 investors. The results demonstrate the effectiveness and usability of PrettiSmart in facilitating an easy interpretation of smart contracts.