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Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.

The purpose of this paper is to investigate whether immigrants in the USA receive an earnings premium associated with marrying a native.

The raw premium revealed by the 2000 US Census data is suspect due to possible endogeneity and selection bias. Instrumental variables estimation, a sample selection model, and a counterfactual construction method are used to address these issues.

Results suggest a positive and modest intermarriage premium, although the magnitude varies with the estimation technique. The evidence is particularly strong for immigrants with high English proficiency, college graduates, and immigrants older than 12 upon arrival in the USA.

It is shown that the size of intermarriage premiums varies significantly across different immigrant groups. The empirical results provide insights into the economic assimilation process and mechanisms through which intermarriage influences the labor market outcomes of immigrants.

This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori.

This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time.

Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy.

The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

This paper aims to incorporate one intelligent particle swarm optimization (IPSO) controller to realize an optimum path in unknown environments. In this paper, the fitness function of IPSO is designed with intelligent design parameters, solving the path navigation problem of an autonomous wheeled robot towards the target point by avoiding obstacles in any unknown environment.

This controller depends on randomly oriented positions with all other position information and a fitness function. Evaluating the position’s best values, this study gets the local best values, and finally, the global best value is updated as the current value after comparing the local best values.

The path navigation of the proposed controller has been compared with particle swarm optimization algorithm, BAT algorithm, flower pollination algorithm, invasive weed algorithm and genetic algorithm in multiple challenging environments. The proposed controller shows the percent deviation in path length near 14.54% and the percent deviation in travel time near 4% after the simulation. IPSO is applied to optimize said parameters for path navigation of the wheeled robot in different simulation environments.

A hardware model with a 32-bit ARM board interfaced with a global positioning system (GPS) module, an ultrasonic module and ZigBee wireless communication module is designed to implement IPSO. In real-time, the IPSO controller shows the percent deviation in path length near 9%.

Path planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots.

First, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking.

The simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework.

The work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.

The paper aims to investigate performance benefits associated with adopting a mobile wireless sensor network (WSN). Sensor nodes are generally energy constrained due to the latter being acquired from onboard battery cells. If one or more sensor nodes fail, possible coverage holes may be created which could invariantly lead to a reduced network lifetime. The paper proposes that instead of rendering the entire WSN inoperative, sensor nodes should physically change position within the region of interest thus adaptively altering the WSN topology with a view of recovering from failures. This type of motion will be referred to as “self healing”.

This paper presents a mobility scheme based on Bayesian networks for predictive reasoning (BayesMob) which is essentially a distributed self healing algorithm for coordinating physical relocation of sensor nodes. Using the algorithm, sensor nodes can predict the performance of the WSN in terms of coverage given that the node moves in a given direction. The evidence for this hypothesis is acquired from local neighborhood information.

The paper compares BayesMob with an alternative algorithm – Coverage Fidelity Algorithm – and shows that BayesMob maintains a higher level WSN coverage for a greater percentage of failures, thus increasing the useful lifetime of the WSN.

The physical relocation of sensor nodes will incur energy overhead, therefore the tradeoffs between all application criteria should be investigated before implementation.

This paper presents a Bayesian network based motion coordination algorithm for WSN which repairs coverage holes caused by energy exhaustion and/or abrupt node failures.

Considers the effects on the mass media of China’s transition to a more open economy and the strategies which advertisers might use to overcome the problems it presents. Describes the media available (including the unofficial “black route”, the official but restricted private sector “white route”, and the state “red route”) and lists the challenges which advertisers face in using them. Suggests some strategies and practices which can help to overcome them and some hypotheses on future media use. Calls for further research in this area.

The purpose of this paper is to examine the relationship between dividend policy and earnings quality of Vietnamese listed firms.

The sample includes firms listed on Vietnam stock exchange during the period between 2010 and 2016. Two measures of earnings quality are the annual firm-specific absolute value of residuals from Dechow and Dichev’s (2002) model and from Dechow and Dichev (2002) as modified by McNichols’s (2002) model. The firms’ dividend policy is captured by dividend paying status. This is a dummy variable that takes the value of 1 if the firm pays dividends and 0 otherwise. In addition, dividend yield and dividend payout ratio, which are continuous variables, are also used in this paper as alternative proxies for dividend policy.

Using panel data analysis, this paper documents that dividend payers have higher earnings quality than dividend non-payers. Dividends are an indicator of earnings quality. These findings are consistent with prior studies. After controlling for variables that may be related to earnings quality as well as for the year and industry fixed effects, this relation remains unchanged. In addition, this result is also robust after controlling for firm fixed effects.

This paper offers the empirical evidence on the relation between dividend policy and earnings quality in Vietnam, which is a frontier market.

The conceptual framework developed in the present study aims to highlight the importance of human resource (HR) practices as a mediator between national culture and employees' careers.

The approach taken is a literature review and the development of a conceptual model.

The paper contributes to the literature by focusing on how culture via HR practices might influence career success. Drawing on Hofstede's cultural dimensions, five propositions are developed regarding the impact of culture on career‐relevant HR practices, and how these practices are likely to influence employee career success.

Culture's effect should not be overstated. Looking at the propositions, it is possible that the influence of HR practices on career success is more pronounced than the direct effects of culture on career success. Future work is needed to measure and compare the relative strength of different associations as well as the possibility that other HR dimensions relevant to the study of career success may exist.

At a general level, there is ample evidence of the impact of culture on the effectiveness of a variety of individual outcomes. The paper focused on the mediating role of HR practices as opposed to advancing hypotheses about direct relationships between culture and career success.

The purpose of this paper is to explore the relationship between external monetary incentives (EMI) and affective commitment (AC), normative commitment (NC), and continuance commitment (CC).

Data were obtained from a sample of 880 employees of different education and job levels, industries, sectors, and salary grades.

An increase in EMI value is linked to a weakening of AC. In a non-Western context, specific employee characteristics – salary grade, educational level, industry type, and employment in the private vs public sector – are associated with different levels of CC. At the same time, employees at different job levels (top managers, middle managers, supervisor, and operative-level employees) are differently predisposed toward AC and NC. Job level emerged as a moderating variable between EMI and AC.

Understanding of the ways in which EMI are related to organizational commitment will inform organizational decision makers about how to be more successful in retaining valuable employees.

The study offers a systematic exploratory examination of the relationship between commitment components (AC, NC, and CC) and the amount of salary offered by an alternative employer.