Amid the rapid expansion of the Non-Fungible Token (NFT) market, X (formerly Twitter) has emerged as a crucial channel for communication between project creators and their communities. This study investigates the short-term effects of NFT project tweets on trading behaviors and price dynamics. Guided by Media Richness Theory (MRT), we con-ducted a quantitative analysis of tweets from nine leading NFT projects, categorizing them into three distinct clusters. Our findings reveal heterogeneous correlations between tweet content, NFT categories, and price fluctuations. The differing roles and functions of NFTs across categories shape both the distribution of tweets and their short-term pricing impacts. Furthermore, we employed three machine learning models using media richness as a predictive feature, achieving approximately 60 % accuracy in forecasting NFT price movements. Overall, this research highlights the predictive potential of social media for NFT price trends and its contribution to the NFT ecosystems sustainability.

We demonstrate ModsNet , a search tool for pre-trained data science MOD el s recommendatio N using E xamplar da T aset. Given a set of pre-trained data science models, an "example" input dataset, and a user-specified performance metric, ModsNet answers the following query: "what are top-k models that have the best expected performance for the input data?" The need for searching high-quality pre-trained models is evident in data-driven analysis. Inspired by "query by example" paradigm, ModsNet does not require users to write complex queries, but only provide an "examplar" dataset, a task description, and a performance measure as input, and can automatically suggest top- k matching models that are expected to have desirable performance to perform the task over the provided sample dataset. ModsNet utilizes a knowledge graph to integrate model performances over datasets and synchronizes it with a bipartite graph neural network to estimate model performance, reduce inference cost, and promptly respond to top- k model search queries. To cope with strict cold-start (upon receiving a new dataset when no historical performance of registered models are observed), it performs a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising models. We demonstrate the application of ModsNet in enabling efficient scientific data analysis.

Data analytical pipelines routinely involve various domain-specific data science models. Such models require expensive manual or training effort and often incur expensive validation costs (e.g., via scientific simulation analysis). Meanwhile, high-value models remain to be ad-hocly created, isolated, and underutilized for a broad community. Searching and accessing proper models for data analysis pipelines is desirable yet challenging for users without domain knowledge. This paper introduces ModsNet, a novel MODel SelectioN framework that only requires an Example daTaset. (1) We investigate the following problem: Given a library of pre-trained models, a limited amount of historical observations of their performance, and an "example" dataset as a query, return k models that are expected to perform the best over the query dataset. (2) We formulate a regression problem and introduce a knowledge-enhanced framework using a model-data interaction graph. Unlike traditional methods, (1) ModsNet uses a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising pre-trained models in a strict cold-start scenario (when a new dataset without any interaction with existing models is given). (2) To reduce the learning cost, we develop a clustering-based sparsification strategy to prune unpromising models and their interactions. (3) We showcase of ModsNet built on top of a crowdsourced materials knowledge base platform. Our experiments verified its effectiveness, efficiency, and applications over real-world analytical pipelines.

Modern multidisciplinary materials science routinely processes scientific workflows that integrate different data resources (e.g., X-ray data, scripts, analytical results). Most of such data resources are isolated in research labs, created ad-hocly, and remain underutilized. We demonstrate CRUX, a Crowdsourced platform for materials data ResoUrces and workflow eXploration. CRUX is empowered by coherent data-workflow modeling, knowledge-based resource assembly for workflow search, and data provenance to support workflow exploration. CRUX allows users to declare parameterized workflows as graph patterns, and automatically recommends crowdsourced resources with quality guarantees. We demonstrate the ease-of-use and the performance of CRUX with three categories of queries: data search, workflow recommendation, and resource exploration. We make case of CRUX for peak finding in X-ray Diffraction (XRD) data, a cornerstone task in materials research. We show that CRUX enables new interactive paradigms to explore and design workflows for data analysts in general.

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey, we examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines.

Recent advancement of connected vehicles technologies combined with machine learning (MA) methods has shown great potential for the improvement of efficiency of Intelligent Transportation System. In this work, considering the spatio-temporal correlations under vehicle distribution on urban road network, neural network based deep learning solution is adopted to obtain vehicle driving characteristics and predict future traffic conditions. First, to address the huge challenge brought by complex traffic environment, we present a fine-grained regional-level forecast structure for the prediction of traffic flow at each road. After that, a residual network based deep learning traffic prediction algorithm called DST-RGTP is proposed for the performance enhancement of vehicle regulation in the entire traffic system. Finally, we use the real traffic data of Beijing and open-source road network data on Openstreetmap to test the proposed method. Simulation results verify the accuracy of prediction approach DST-RGTP, which can help to improve the urban traffic management efficiency.