Room-temperature superconductors (esp. LK-99 in the recent) have attracted extensive academic attention in recent years, both in academic circles and among the general public. This topic has spread through a number of social media channels, a plethora of contradiction in information has emerged within social networks. There arises the question on how to analyze the consistency and controversy of such scientific knowledge in the dissemination process, and how this process impact on public cognition on the scientific knowledge. In this article, taking room-temperature superconductor as example, we first designed a large language model based factual consistency detection approach to analyze the consistency between research papers and media reports. Then the consistency between media reports and comments is analyzed, by proposing a novel quantification method for media agenda-setting capability, which evaluates the agenda-setting capability of media based on emotional and positional consistencies. The results indicate that two significant deviations occur when room-temperature superconductor knowledge is spread from specialized fields to the public through the various media. One deviation is due to the specialized nature of room-temperature superconductor knowledge, leading to discrepancies between reported content and factual information in research papers. The other deviation is caused by conflicting knowledge, resulting in disparities between media reports and public perception.

In February 2024, Lionel Messi's absence from a Hong Kong exhibition match ignited extensive debate across social media platforms, capturing the attention of the public and sparking controversies that extended into realms such as business partnerships and diplomatic implications. This incident not only reflects the public's reaction and pattern of stance changes toward such a complex Incident but also significantly demonstrates the close connection between cyberspace and the real world. Research on this incident has important practical significance for predicting and intervening in the evolution of similar hot incidents. In this article, the case of Messi's absence from the Hong Kong match is delved into. A stance detection method and a quantification approach for stance divergence have been devised to analyze the evolution of the stance surrounding the incident. Furthermore, by examining topic clusters over time, the topical progression of public stances is tracked. Findings reveal that nearly 60% of posts expressed an explicit stance throughout the incident, with the majority (66.9%) taking an opposing stance. Additionally, it was noted that the topics discussed at various stages followed a long-tail distribution, indicating that most discussions revolved around a few dominant themes. Within more segmented and specific topics, stance divergences were often more prominent.

Federated learning is an emerging distributed machine learning paradigm that can break through data silos and make use of data from different clients in a secure way. However, for deep neural networks in federated learning, the models on clients may learn the same pattern with different weight distributions despite the same data distribution of local data sets, which limits the performance of neural networks after weight fusions. Therefore, in this article, we propose a homophily learning-based federated intelligence (HLFI) approach, where hierarchical federated learning strategy and dynamic elimination learning strategy are designed to alleviate the problem. The experiments on equipment failure prediction show that the proposed approach can improve the failure prediction F1-score up to 9.32%. Our approach also has good generalization capabilities and can be applied in other federated learning methods to improve the model performance.

Person–job fit, which aims to predict the matching degree between a resume and a job, has become an effective way to overcome information overload in the recruitment market. Existing studies on person–job fit usually focus on the representation learning of textual data in jobs and resumes. Person–job fit is a highly nonlinear complex problem that is affected by several fields of features. We assume that it would bring benefits to comprehensively consider the numerical features, categorical features, and textual features of resumes and jobs. To this end, we propose a novel model based on the self-attention mechanism, named MUlti-Field Features representation and INteraction (MUFFIN) learning for person–job fit. The key idea is to explore meaningful feature representations and interactions. Specifically, we group all the features of resumes and jobs into several fields. And a module is introduced to learn the hidden vectors of feature correlations in each feature field. Along this line, we propose a module with the multi-head self-attention mechanism and a residual connection to further model the feature field interactions. Moreover, we utilize a multi-layer perceptron (MLP) to measure the matching score between a resume and a job. Finally, the experimental results on a real-world dataset validate the effectiveness of MUFFIN for person–job fit.

Employees' expertise is the basic component of human capital of organizations. As the role of human capital and the understanding of employee development become increasingly vital, research works about the effects of working expertise on development are necessary. This article aims to confirm the effect of expertise and find out how expertise affects development. In this article, we analyze employee development and working expertise through data-driven methods, using a data set of a Chinese state-owned enterprise. In addition to statistical analysis, expertise networks are constructed to discover more insights about the effect of expertise on employee development. Moreover, to further validate and exploit the effect, a prediction model of development potential is proposed based on machine learning. Results of the experiment show that the random forests model with network embedding (RFNE) is effective in identifying excellent employees. Finally, with the help of data-driven analysis of expertise and development, we find that the appropriate post, the right choice, the distinctive competency, as well as the interdisciplinary transfer contribute to employee development.

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.

To address the large-scale and time-consuming multiple agile earth observation satellite (multi-AEOS) scheduling problems, this article proposes a data-driven parallel scheduling approach, which is composed of a probability prediction model, a task assignment strategy, and a parallel scheduling manner. In this approach, given the historical data of satellite scheduling, a prediction model is trained based on the cooperative neuro-evolution of augmenting topologies (C-NEAT) to predict the probabilities that a task will be fulfilled by different satellites. Driven by the probability prediction model, an assignment strategy is adopted for dividing the multi-AEOS scheduling problem into several single-AEOS scheduling subproblems, which can adaptively assign each task to the satellite with the highest predicted probability and greatly decrease the problem size. In a parallel manner, the single-AEOS scheduling subproblems are optimized, respectively, leading to an acceleration in the optimization efficiency of the original problem. Computational experiments indicate that the proposed approach presents better overall performance than other state-of-the-art methods within a very limited scheduling time. As the two main components of the proposed approach, the prediction model based on C-NEAT and the task assignment strategy also outperform other models with traditional training algorithms and inadaptive assignment strategies, respectively.

Building autonomous systems that achieve human level intelligence is one of the primary objectives in artificial intelligence (AI). It requires the study of a wide range of functions robustly across different phases of human cognition. This paper presents a review of agent cognitive architectures in the past 20 year's AI research. Different from software structures and simulation environments, most of the architectures concerned are established from mathematics and philosophy. They are categorized according to their knowledge processing patterns-symbolic, emergent or hybrid. All the relevant literature can be accessed publicly, particularly through the Internet. Available websites are also summarized for further reference.

We present a bibliographic and coauthorship-based collaboration analysis of papers published in the IEEE Transactions on Intelligent Transportation Systems (T-ITS) between 2014 and 2016 from the aspects of productivity, topics, citations, usage, and coauthorship networks. The most productive authors, institutions, countries/regions, and the most cited papers, most popular papers, as well as the most frequent topics and their trends are identified and analyzed. Social network methods are employed for revealing collaboration patterns among contributors through author- and institution-level coauthorship. The results show that China is playing a critical role in ITS research during this period but the interinstitution collaborations are less prevalent than it used to be. Overall data have indicated that IEEE T-ITS has made tremendous progress and contributed significantly to the accelerated growth of ITS fields over the last three years.

The construction of knowledge graph of dangerous goods (KGDG) is with great significance of inferring relative information of dangerous goods, developing corresponding policy for its storage and transport, preventing disaster caused by dangerous goods(DG), and providing emergency plan when the disaster happens. Since distributed representation of natural language is an effective method for knowledge representation, we proposed a distributed method of relation extraction for constructing KGDG. We firstly automatically crawled the description of various DG from web to obtain a large corpus. Secondly, we cut the words and represented them by training an embedding vector matrix. Thirdly, we extracted the relation among entities of DG based on similarity of any two words. At last, we compared the performance of relation extraction between co-occurrence and embedding vector. The results showed that our method works well for constructing KGDG.

In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems.