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A fundamental problem for intelligent manufacturing is to equip the agents with the ability to automatically make judgments and decisions. This paper aims to introduce the basic principle for intelligent crowds in an attempt to show that crowd wisdom could help in making accurate judgments and proper decisions. This further shows the positive effects that crowd wisdom could bring to the entire manufacturing process.

Efforts to support the critical role of crowd wisdom in intelligent manufacturing involve theoretical explanation, including a discussion of several prevailing concepts, such as consumer-to-business (C2B), crowdfunding and an interpretation of the contemporary Big Data mania. In addition, an empirical study with three business cases was conducted to prove the conclusion that our ideas could well explain the current business phenomena and guide the future of manufacturing.

This paper shows that crowd wisdom could help make accurate judgments and proper decisions. It further shows the positive effects that crowd wisdom could bring to the entire manufacturing process.

The paper highlights the importance of crowd wisdom in manufacturing with sufficient theoretical and empirical analysis, potentially providing a guideline for future industry.

Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to improve the medication adherence rate. However, most of the existing electronic pillboxes use time-based reminders which may often lead to ineffective reminding if the reminders are triggered at inopportune moments, e.g. user is sleeping or eating.

In this paper, the authors propose an AI-empowered context-aware smart pillbox system. The pillbox system collects real-time sensor data from a smart home environment and analyzes the user’s contextual information through a computational abstract argumentation-based activity classifier.

Based on user’s different contextual states, the smart pillbox will generate reminders at appropriate time and on appropriate devices.

This paper presents a novel context-aware smart pillbox system that uses argumentation-based activity recognition and reminder generation.

Based on the survey data of 1,152 households in three provinces of Shaanxi, Gansu and Ningxia on the Loess Plateau, this paper aims to empirically analyze the impact of aging and off-farm employment on farmers’ adoption behavior of soil and water conservation technology. This paper analyzes the moderating effect of social network and the mediating effect of technological cognition in this impact relationship.

Based on the above analysis, the second part of this paper is based on relevant theories and constructs a theoretical model of the relationship of aging, off-farm employment, social network, technology cognition and farmers’ adoption behavior of soil and water conservation technology. The third part introduces research methods, variable selection and descriptive statistics analysis of variables. The fourth part, based on the data of Shaanxi, Gansu and Ningxia provinces in the Loess Plateau in 2016, empirically analyzes the impact of aging, off-farm employment and social network on the farmers’ adoption behavior of soil and water conservation technology. This paper further examines the moderating effect of social network and the mediating effect of technology cognition in this influence relationship. Finally, based on the findings of the empirical study, this paper puts forward countermeasures and suggestions.

First, aging and off-farm employment have a significant negative impact on farmers’ adoption behavior of soil and water conservation technology, while social network has a significant positive effect. Second, social network has alleviated the effect of aging and off-farm employment on restraining farmers’ adoption behavior of soil and water conservation technology. Third, aging and off-farm employment have restrained farmers’ cognition of soil and water conservation technology. Social network has promoted farmers’ cognition of soil and water conservation technology. Social network plays a moderating role in the impact of aging and off-farm employment on farmers’ cognition of soil and water conservation technology. Technology cognition plays a mediating role in the impact of social network on farmers’ adoption behavior of soil and water conservation technology.

This paper integrates the aging, off-farm employment and social network into the same analytical framework and reveals their impact on farmers’ adoption behavior of soil and water conservation technology and its action mechanism, which enriches the impact of human capital and social network on farmers’ adoption behavior of soil and water conservation technology. Then taking the social network as a moderator variable, the paper verifies its moderating effect on the relationship of aging, off-farm employment and farmers’ adoption behavior of soil and water conservation technology. Farmers’ technology cognition should be included in the analysis framework to examine the impact of aging, off-farm employment and social network on farmers’ cognition of soil and water conservation technology. Taking the technology cognition as a mediator variable, the paper verifies its mediating effect on the relationship of aging, off-farm employment and farmers’ adoption behavior of soil and water conservation technology.

The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.

In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.

Based on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.

With the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

This article explores the scientific construction of library olfactory space, based on the case of the olfactory space in the Jiangsu University library. It specifically focuses on understanding the interaction between the physical architectural space of the library and users’ olfactory perception and behavioral activities, with the ultimate goal of creating a deeply integrated olfactory experience in the Jiangsu University Library.

In this article, an empirical research method was used to gather perceptions from 30 university student users regarding the library olfactory space and to understand their olfactory preferences and requirements for its construction. Through qualitative analysis of the interview texts, the study identified correlations between user perceptions and elements of the library olfactory space.

The qualitative analysis of user interview texts and results from the library olfactory space design experiment contributed to the design proposal for the Jiangsu University Library olfactory space. The design proposal for the Jiangsu University Library olfactory space is provided and includes library architecture, activity context, functional services, olfactory experience design and technological applications.

This case study takes the environment, development strategy and user needs of the Jiangsu University Library as its unique research background and as such is not universal or generalizable to other libraries.

This article differs from others by advocating for the innovative architectural spatial design of libraries through olfactory experience, breaking the traditional perception of libraries as solely through visual and auditory senses.

The purpose of this study is to evaluate the effectiveness of one online PD in PBL. Researchers want to investigate if a five-day international online PBL training will prepare teachers to implement PBL in their classrooms. Secondly, the researchers aim to determine if the training provides teachers with sufficient knowledge and support to ensure successful PBL implementation.

Participants were given a 5-day (20 h) online PBL training created by one of the researchers with three frontline teachers. Seven trainers are divided into four groups for four groups of participants. Group A included Grade 1 and Grade 2 teachers, Group B included Grade 3 and Grade 4 teachers, Group C included Grade 5 and Grade 6 teachers, and Group D consisted of Grades 7 through 9 teachers. All the participants were given exactly the same surveys at the beginning and the end of the training.

Consistent with previous studies comparing in person and virtue PD programs, this five-day interactive PD program was effective in increasing teachers' knowledge of and ability to plan and implement PBL projects. Specifically, results showed that teachers' knowledge level of PBL shifted from a shallow understanding of what the name implies to a deeper, more comprehensive, and more concrete understanding of PBL essential concepts, its pedagogical values, specific process involved in a PBL project. In addition, the PD program increased teachers' comfort level and ability of planning and implementing PBL projects across grade levels and subject areas.

This research study supported the previous study results that virtual PD programs can be as effective as in person programs. Further, this is the study discovered the effectiveness of PBL training between the US and China through online format, which has not been conducted literately before. The positive results will be used to promote the online collaboration internationally in the future.

Allocation of complex crowdsourcing tasks, which typically include heterogeneous attributes such as value, difficulty, skill required, effort required and deadline, is still a challenging open problem. In recent years, agent-based crowdsourcing approaches focusing on recommendations or incentives have emerged to dynamically match workers with diverse characteristics to tasks to achieve high collective productivity. However, existing approaches are mostly designed based on expert knowledge grounded in well-established theoretical frameworks. They often fail to leverage on user-generated data to capture the complex interaction of crowdsourcing participants’ behaviours. This paper aims to address this challenge.

The paper proposes a policy network plus reputation network (PNRN) approach which combines supervised learning and reinforcement learning to imitate human task allocation strategies which beat artificial intelligence strategies in this large-scale empirical study. The proposed approach incorporates a policy network for the selection of task allocation strategies and a reputation network for calculating the trends of worker reputation fluctuations. Then, by iteratively applying the policy network and reputation network, a multi-round allocation strategy is proposed.

PNRN has been trained and evaluated using a large-scale real human task allocation strategy data set derived from the Agile Manager game with close to 500,000 decision records from 1,144 players in over 9,000 game sessions. Extensive experiments demonstrate the validity and efficiency of computational complex crowdsourcing task allocation strategy learned from human participants.

The paper can give a better task allocation strategy in the crowdsourcing systems.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

The purpose of this paper is to build cyber-physical-psychological ternary fusion crowd intelligence network and realize comprehensive, real, correct and synchronous projection in cyber–physical–psychological ternary fusion system. Since the network of crowd intelligence is the future interconnected network system that takes on the features of large scale, openness and self-organization. The Digital-selfs in the network of crowd intelligence interact and cooperate with each other to finish transactions and achieve co-evolution eventually.

To realize comprehensive, real, correct and synchronous projection between cyber–physical–psychological ternary fusion system, the authors propose the rules and methods of projection from real world to the CrowdIntell Network. They build the mental model of the Digital-self including structure model and behavior model in four aspects: identity, provision, demand and connection, thus forming a theoretical mental model framework of Digital-self.

The mental model is excepted to lay a foundation for the theory of modeling and simulation in the research of crowd science and engineering.

This paper is the first one to propose the mental model framework and projection rules and methods of Digital-selfs in network of crowd intelligence, which lays a solid foundation for the theory of modeling, simulation, intelligent transactions, evolution and stability of CrowdIntell Network system, thus promoting the development of crowd science and engineering.

This study aims to propose a speed guidance model of the CV environment to alleviate traffic congestion at intersections and improve traffic efficiency. By introducing the theory of moving block section for high-speed train control, a speed guidance model based on the quasi-moving block speed guidance (QMBSG) is proposed to direct platoon including human-driven vehicles and connected vehicles (CV) through the intersection coordinately.

In this model, the green time of the intersection is divided into multiple block intervals according to the minimal safety headway. Connected vehicles can pass through the intersection by following the block interval using the QMBSG model. The block interval is assigned dynamically according to the traveling relation of HV and CV, when entering the communication range of the intersection. To validate the comprehensive guidance effect of the proposed model, a general evaluation function (GEF) is established. Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement.

Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Also, compared to the single intersection speed guidance model, the GEF value of the QMBSG model improves over 17.1%. To further explore the guidance effect, the impact of sensitivity factors of the CVs’ environment, such as intersection environment, communication range and penetration rate (PR) is analyzed. When the PR reaches 75.0%, the GEF value will change suddenly and the model guidance effect will be significantly improved. This paper also analyzes the impact of the length of block interval under different PR and traffic demands. It is found that the proposed model has a better guidance effect when the length of the block section is 2 s, which facilitates traffic congestion alleviation of the intersection in practice.

Based on the aforementioned discussion, the contributions of this paper are three-fold. Based on the traveling information of HV/CV and the signal phase and timing plans, the QMBSG model is proposed to direct platoon consisting of HV and CV through the intersection coordinately, by following the block interval assigned dynamically. Considering comprehensively the indexes of mobility, safety and environment, a GEF is provided to evaluate the guidance effect of vehicles through the intersection. Sensitivity analysis is carried out on the QMBSG model. The key communication and traffic parameters of the CV environment are analyzed, such as path attenuation, PR, etc. Finally, the effect of the length of block interval is explored.