This study analyzes the mechanism through which ambidextrous innovation influences technological catch-up in latecomer enterprises. It first proposes a conceptual model of the influence of ambidextrous innovation on technological catch-up in latecomer enterprises and introduces the two regulating variables of alliance governance model and absorptive capacity. It then empirically studies the main effect and adjustment effect using hierarchical regression analysis based on a large-sample questionnaire survey, and finally, determines the effect and conditions of ambidextrous innovation on technological catch-up in latecomer enterprises.

We combine the Apriori data mining algorithm and smooth cut-point calculation to build a model that uses microscopic individual data to predict fertility behavior. The data of China’s migrant population from 2013 to 2015 are used to predict the reproductive behavior of migrant women. The accuracy of the prediction results is over 84%. The model also quantifies the extent to which the existing characteristics of individuals influence their reproductive behavior. The government can regulate individual fertility behavior based on the quantified scores.

It is of great significance to forecast the intraday returns of stock index futures. As the data sampling frequency increases, the functional characteristics of data become more obvious. Based on the functional principal component analysis, the functional principal component score was predicted by BM, OLS, RR, PLS, and other methods, and the dynamic forecasting curve was reconstructed by the predicted value. The traditional forecasting methods mainly focus on “point” prediction, while the functional time series forecasting method can avoid the point forecasting limitation, and realize “line” prediction and dynamic forecasting, which is superior to the traditional analysis method. In this paper, the empirical analysis uses the 5-minute closing price data of the stock index futures contract (IF1812). The results show that the BM prediction method performed the best. In this paper, data are considered as a functional time series analysis object, and the interference caused by overnight information is removed so that it can better explore the intraday volatility law, which is conducive to further understanding of market microstructure.

This study provides an in-depth analysis of the impact of innovation on industrial upgrading from theoretical and practical perspectives. In terms of theory, based on the endogenous growth theory model, a multi-sectoral growth model is constructed to portray the inherent logical connection between innovation and industrial structure upgrading. The results show that innovation has an important impact on industrial structure upgrading. Industrial structure upgrading depends on differences in innovation level among industries and the substitution relationship of finished products in the industry. From an empirical perspective, based on the panel data of 31 provinces in China from 2005 to 2017, the spatial effect of innovation on industrial upgrading was analyzed using the spatial Durbin model. The results show that innovation and industrial structure upgrading has significant spatial correlation effects, and regional innovation can drive China’s industrial upgrading. Meanwhile, the space spillover effect is an important factor that cannot be ignored in industrial upgrading.

In this study, a wavelet denoising method is first used to eliminate the influence of noise. Then, an overlapping smooth window technique is introduced into the asymmetric multifractal detrended cross-correlation analysis method, which was combined with the multiscale multifractal analysis method, resulting in the proposed asymmetric multiscale multifractal detrended cross-correlation analysis method. This method not only remedies the pseudo-fluctuation defect of the traditional method, but also explores the asymmetric multifractal cross-correlation between China’s rebar futures and spot markets at different scales. The results show the existence of an asymmetric multifractal cross-correlation between rebar futures and spot markets with upward and downward trends at different scales. This cross-correlation is highly complex at the small-scale, and more pronounced when the futures market is in an uptrend.

For those who love sports, not only appearance but also functionality are important considerations in the design of athletic shoes. This is a study done on 12 subjects on a college fencing team and other sports teams. The subjects wear experimental specialized athletic shoes, including fencing shoes and other similar athletic shoes. Five kinds of shoes, fencing shoes plus four other similar types of athletic shoes, were examined for their shock absorption and rebound capacities. No significant difference between was found among them in the lunge test. However, there were significant differences between the two types of shoes in the 15 cm and 35 cm jump-down tests and extra heel support silicone gaskets in the fencing-like shoes tests. The fencing shoes proved to be the worst in terms of rebound ability in the 35 cm jump-down test. The fencing shoes had the best shock absorption but the worst rebound ability. In terms of overall performance, fencing shoes had the best shock absorption capacity. It is advisable for fencing shoes to be required during fencing training and at tournaments, but the severity of bounces, jumps, and squats should be lowered to avoid damage to the heel.

Numerous studies have proven that news media sentiment has an impact on stock market volatility, making topics such as how to quantify news media sentiment and use it to predict stock market volatility increasingly relevant. In this paper, a Chinese financial sentiment lexicon was constructed to quantify the emotions in the news media as a sentiment index to be added to the model and establish new prediction models HAR-RV-AI and GRU-AI. To compare the prediction ability of the models, we consider the loss function and model confidence set (MCS) test as the evaluation criterion and employ the rolling window strategy for out-of-sample forecasting. The prediction results of the GRU model are found to be better than the HAR-RV model, and the prediction effect of the model improved after the addition of the news media sentiment index.

Objectives: To examine self-esteem in college students who participate in physical education (PE), the influence of psychological trends on self-esteem, and the possible influence of passion. Motivations: This study aims to determine whether passion affects participants’ self-esteem and psychological well-being. Methods: Subjects included 183 students enrolled in university PE classes (46 men and 137 women) with an average age of 19.58 (SD = 6.19). Self-esteem, passion, and psychological well-being were measured using a fuzzy questionnaire and descriptive statistics and structural equation modeling were performed through fuzzy statistical analysis. Findings: 1. The self-esteem, passion, and psychological well-being scales had good reliability and validity. 2. The model’s dimensional goodness of fit was satisfactory (χ² = 281.601(182) = 1.952, GFI = 0.863, AGFI = 0.821, RMSEA = 0.072, SRMR = 0.649, TLI [NNFI] = 0.918, IFI = 0.925, CFI = 0.942). 3. Passion had a direct influence on well-being and an indirect influence on self-esteem. Well-being had a direct influence on self-esteem. Innovations: The passion scale and psychological well-being scale were confirmed to have good reliability and validity. The new method of fuzzy statistical analysis used in this study provides new research techniques for investigation and research into psychological trends in the field of sports. Value: In the teaching process, PE teachers should encourage students’ passion for participation to better their psychological well-being and self-esteem.

This paper estimates users’ search intents when using the context search engine (CSE) by analyzing submitted queries. Recently, due to the increase in the amount of information on the Web and the diversification of information needs, the gap between user’s information needs and a basic search function provided by existing web search engines becomes larger. As a solution to this problem, the CSE that limits its tasks to answer questions about temporal trends has been proposed. It provides three primitive search functions, which users can use in accordance with their purposes. Furthermore, if the system can estimate users’ search intents, it can provide more user-friendly services that contribute the improvement of search efficiency. Aiming at estimating users’ search intents only from submitted queries, this paper analyzes the characteristics of queries in terms of typical search intents when using CSE, and defines classification rules. To show the potential use of the estimated search intents, this paper introduces a learning to rank into CSE. Experimental results show that MAP (mean average precision) is improved by learning rank models separately for different search intents.

This paper proposes a personal values-based user modeling method from user’s browsing history of reviews. Personal values-based user modeling and its application to recommender systems have been studied. This approach models users’ personal values as the effect of item’s attributes on their decision making. While existing method obtains a user model from reviews posted by a user, this paper proposes to obtain it from reviews a user consulted for his/her decision making. Methods for determining reviews to present for obtaining user feedback, as well as for selecting items to recommend are proposed, of which effectiveness are shown with user experiments.

A selective encryption algorithm is proposed to improve the efficiency of high efficiency video coding (HEVC) video encryption and ensure the security of HEVC videos. The algorithm adopts the integer dynamic coupling tent mapping optimization model as the pseudo-random sequence generator, and multi-core parallelization is used as the sequence generation mechanism. The binstrings during the process of context adaptive binary arithmetic coding are selected for encryption, which conforms to the features of invariable binstream and compatible format in terms of video encryption. Performance tests for six types of standard videos with different resolutions were performed. The results indicated that the encryption algorithm has a large key space and benefits from a high encryption effect.

Mobile robots equipped with camera sensors are required to perceive humans and their actions for safe autonomous navigation. For simultaneous human detection and action recognition, the real-time performance of the robot vision is an important issue. In this paper, we propose a robot vision system in which original images captured by a camera sensor are described by the optical flow. These images are then used as inputs for the human and action classifications. For the image inputs, two classifiers based on convolutional neural networks are developed. Moreover, we describe a novel detector (a local search window) for clipping partial images around the target human from the original image. Since the camera sensor moves together with the robot, the camera movement has an influence on the calculation of optical flow in the image, which we address by further modifying the optical flow for changes caused by the camera movement. Through the experiments, we show that the robot vision system can detect humans and recognize the action in real time. Furthermore, we show that a moving robot can achieve human detection and action recognition by modifying the optical flow.

A pneumatic gravity compensation system is typically nonlinear in behavior. It is difficult to establish an accurate mathematical model for it, and it is particularly difficult to realize high-precision pressure control. A pneumatic gravity compensation system driven by a frictionless cylinder is built. Considering that the traditional model-free adaptive control is slow for pseudo-gradient identification, an improved model-free adaptive control is proposed to predict the changes in the pseudo gradient and accelerate the process of pseudo gradient identification. The static and dynamic gravity compensation of the pneumatic gravity compensation system is realized. Finally, the experimental results show that the steady-error of step response of the improved model-free adaptive controller is less than 200 Pa, and the rise time is approximately 13 seconds. The sinusoidal tracking error (0.04 Hz) is approximately 1.94 KPa.

In recent years, computers, artificial agents, intelligent robots, and other intelligent systems have become normal parts of our everyday lives, so high interpersonal affinity, including smooth communications and bidirectional interactions with people, have become necessary for the development of intelligent systems.

“Human Symbiotic Systems” (HSS) is an area of research to study the basic principles and methods used in the designing of intelligent interaction systems, or systems with bidirectional communications based on the symbiosis and effective collaboration between people and robots, agents, computers, etc. In HSS research, the establishment of elemental technologies necessary for the realization of intelligent systems to coexist with people is a main target.

The research society “Human Symbiotic Systems” was established in the Japan Society for Fuzzy Theory and Intelligent Informatics in 2007 by the guest editors of this special issue. The HSS aims to encourage academic and industrial discussions on research of Human-Agent Interaction (HAI), Human-Robot Interaction (HRI), Human-Computer Interaction (HCI), etc.

The aim of this special issue is to activate and expand top-quality research in the area of the theory and applications of Human Symbiotic System (HSS). This special issue “Human Symbiotic Systems II” (HSS-II) is the second special feature of the HSS. The first special issue appeared as JACIII journal Vol.14, No.7 in 2010. Various technologies have been developed in the past ten years, but HSS continues to be an important area of study in the field of engineering today.

For this special issue on HSS-II, 10 papers were received, and 7 papers were accepted for publication after two peer reviews each. We would like to thank the authors and referees for their great efforts. Through their contributions, this special issue was made possible and overall paper quality was improved.

This paper proposes an automatic driving system based on a combination of modular neural networks processing human driving data. Research on automatic driving vehicles has been actively conducted in recent years. Machine learning techniques are often utilized to realize an automatic driving system capable of imitating human driving operations. Almost all of them adopt a large monolithic learning module, as typified by deep learning. However, it is inefficient to use a monolithic deep learning module to learn human driving operations (accelerating, braking, and steering) using the visual information obtained from a human driving a vehicle. We propose combining a series of modular neural networks that independently learn visual feature quantities, routes, and driving maneuvers from human driving data, thereby imitating human driving operations and efficiently learning a plurality of routes. This paper demonstrates the effectiveness of the proposed method through experiments using a small vehicle.

With the growth of robot technology, more educational-support robots, which support learning, are paid attention to. For example, one robot supports the school life of students. Another robot helps students to learn English better. Most researches have focused on robot behavior and investigating the effect. Previous research has reported that a society in which robots and humans learn together will soon be a reality. If a society where robots and humans learn side by side is realized, children will be in houses where they will learn alongside multiple unspecified robots. We think that perspectives of third parties, such as educators and guardians, are important for further improvements in the field. Thus, we think that a system is necessary wherein third parties can direct robots to provide suitable learning support to learners. This paper proposes a system for teachers that can direct robots to provide suitable learning support to learners, who simultaneously can grasp their learning conditions as they study alongside robots.

In recent years, much research on the unmanned control of a moving vehicle has been conducted, and various robots and motor vehicles moving automatically are being used. However, the more complicated the environment is, the more difficult it is for the autonomous vehicles to move automatically. Even in such a challenging environment, however, an expert with the necessary operation skill can sometimes perform the appropriate control of the moving vehicle. In this research, a method for learning a human’s operation skill using a convolutional neural network (CNN) and setting visual information for input is proposed for learning more complicated environmental information. A CNN is a kind of deep-learning network, and it exhibits high performance in the field of image recognition. In this experiment, the operation knowledge was also visualized using a fuzzy neural network with obtained input-output maps to create fuzzy rules. To verify the effectiveness of this method, an experiment involving operation skill acquisition by some subjects using a drone control simulator was conducted.

With advances in robotic technology, more robots are being designed to support learning. Most studies have focused on robot behavior and investigated their effects. However, few have studied the compliments given by robots. It is not known how such compliments affect learning and motivation. Therefore, this study investigates the effects of collaborative learning with a robot that delivers compliments. We conducted an experiment to compare the learning effects across three groups. In one group, the learners studied with a robot that praised them using onomatopoeias. In the second group, the learners studied with a robot that praised them using adjectives. In the last group, the learners learned with a robot that praised them without using onomatopoeias or adjectives (original text). The results of this study suggest that collaborative learning with a robot that encourages learners using the original text or onomatopoeias is more effective.

Many studies on human–computer interaction have demonstrated that the visual appearance of an agent or a robot significantly influences people’s perceptions and behaviors. Several studies on the appearance of agents/robots have concluded that consistency between expectations from an agent’s or a robot’s appearance and performances was an important factor to the continuous use of these agents/robots. This is because users would stop interacting with the agents/robots when predictions are not met by actual experiences. However, previous studies mainly focused on the consistency between an initial expectation and a performance of a single instance of a task. The influence of the orders of successes or failures for more than one instance of a task has not been examined in detail. Therefore, in this study, we investigate the order effects of how the timing of sufficient or insufficient results of animated agents affects user evaluation. This will lead to the contribution to fill the lack of studies regarding more than one task in the field of human–computer interaction and to realize the continuous use of agents/robots as long as possible and to avoid stopping to use the agents/robots owing to their successful design. We create a simulated retrieval website and conduct an experiment using retrieval assistant agents that show both sufficient and insufficient results for more than one instance of retrieval tasks. The experimental results demonstrated a recency effect wherein the users significantly revised their evaluations of the animated agents based on new information more than that based on initial evaluations. The investigation of the case of repeated instances of a task and the influence of successes or failures is important for designing intelligent agents that may show incomplete results in intelligent tasks. Furthermore, the result of this study will contribute to build strategies to design behaviors of agents/robots that have a high or low evaluation based on their appearance in advance to prevent users from stopping use of the agents/robots.

There have been recent attempts to control home electric appliances and devices using robots. Information can be shared with robots by using finger pointing. Finger pointing is used as a means of communication with people around. However, when a person points at an object with a finger, position of the object cannot be indicated accurately. In this work, we studied the error between a target point, which a person tries to point at with a finger, and an observation point, which is actually pointed at. We also proposed an error estimation model using a fuzzy integral to estimate and correct the error at the observation point.

Studies on automatic dialogue systems, which allow people and computers to communicate with each other using natural language, have been attracting attention. In particular, the main objective of a non-task-oriented dialogue system is not to achieve a specific task but to amuse users through chat and free dialogue. For this type of dialogue system, continuity of the dialogue is important because users can easily get tired if the dialogue is monotonous. On the other hand, preceding studies have shown that speech with humorous expressions is effective in improving the continuity of a dialogue. In this study, we developed a computer-based humor discriminator to perform user- or situation-independent objective discrimination of humor. Using the humor discriminator, we also developed an automatic humor generation system and conducted an evaluation experiment with human subjects to test the generated jokes. A t-test on the evaluation scores revealed a significant difference (P value: 3.5×10^-5) between the proposed and existing methods of joke generation.