In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, particularly from a holistic perspective on data quality and model capability. Therefore, in this study, we propose SciHorizon, a comprehensive assessment framework designed to benchmark the readiness of AI4Science from both scientific data and LLM perspectives. First, we introduce a generalizable framework for assessing AI-ready scientific data, encompassing four key dimensions-Quality, FAIRness, Explainability, and Compliance-which are subdivided into 15 sub-dimensions. Drawing on data resource papers published between 2018 and 2023 in peer-reviewed journals, we present recommendation lists of AI-ready datasets for Earth, Life, and Materials Sciences, making a novel and original contribution to the field. Concurrently, to assess the capabilities of LLMs across multiple scientific disciplines, we establish 16 assessment dimensions based on five core indicators-Knowledge, Understanding, Reasoning, Multimodality, and Values-spanning Mathematics, Physics, Chemistry, Life Sciences, and Earth and Space Sciences. Using the developed benchmark datasets, we have conducted a comprehensive evaluation of over 50 representative open-source and closed-source LLMs. All the results are publicly available and can be accessed online at www.scihorizon.cn/en.

In today’s competitive and fast-evolving business environment, it is critical for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of big data and artificial intelligence (AI) techniques has revolutionized human resource management (HRM). The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which, in turn, delivers intelligence for real-time decision-making and effective talent management for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for HRM, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of HRM. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios at different levels: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.

In recent decades, innovation and entrepreneurship have become buzz words. In reality, traditional research with empirical results is not practical for analyzing these newly launched projects of small and micro enterprises before production and sale. Actually, the future market prospect is an important criterion for evaluating entrepreneurial projects. However, this direction has not been well explored due to the limitations of scenarios and technical challenges especially for these small and micro enterprises. In this paper, we construct an interesting study of exploiting the market prospect from the sales markets (i.e., E-commerce) to help evaluate newly-posted campaigns in crowdfunding. Specifically, we propose a novel Market-oriented Prospect Analysis with Transferring Attention (MoPa-A) model which contains two learning modules, i.e., HostTask Learning and GuestTask Learning connected and enhanced by transferring attention. The former is designed for funding performance modeling with heterogeneous features of crowdfunding campaigns, and the latter is to represent and transfer the latent semantics of market prospect for target campaigns from campaigns comments with the help of relevant products in sales market. The model design of MoPa-A brings some new insights on flexible knowledge transfer for different or cross domains.

Technology-enhanced learning (TEL) is important in social web. Recently, cognitive computing became significant to analyze sentiment and improve effectiveness in TEL field. So analyzing the development of cognitive computing, what and how its abilities improve TEL are necessary. For solving these issues, this study used systematic review approach based on technology view and enhancement view of TEL. Specifically, this study used topic search results in computer science field of “cognitive computing” and “anticipatory computing” in Web of Science database to do map analysis. Besides development footprints, the manuscript describes three development stages and key technologies of cognitive computing through burst study and step-by-step clustering. Finally, this study proposed influencing framework of cognitive computing on TEL and some research trends. This work provides an advanced background of TEL and a systemic review of cognitive computing, contributing to theory development and application of cognitive computing in TEL.