Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition

As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved can be estimated from these data traces. These estimates are very useful in many applications such as workload statistics and optimized job allocation in the field of logistics. However, due to the uncertainties of the wireless signal and corresponding positioning errors, accurately estimating movement distance still faces challenges. To address this issue, this paper proposes a movement status recognition-based distance estimating method to improve the accuracy. We divide the positioning traces into segments and use an encoder–decoder deep learning-based model to determine the motion status of each segment. Then, the distances of these segments are calculated by different distance estimating methods based on their movement statuses. The experiments on the real positioning traces demonstrate the proposed method can precisely identify the movement status and significantly improve the distance estimating accuracy.

Fast Anther Dehiscence Status Recognition System Establishing by Deep Learning to Screen Heat Tolerant Cotton

BackgroundCotton is one of the most economically important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. The anther dehiscence or indehiscence directly determine the probability of fertilization in cotton. Thus, the rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers. ResultThe single-stage model based on YOLOv5 has higher recognition efficiency and the ability to deploy to the mobile end. Breeding researchers can apply this model to terminals to achieve a more intuitive understanding of cotton anther dehiscence status. Moreover, three improvement strategies of Faster R-CNN model were proposed, the improved model has higher detection accuracy than YOLOv5 model. We have made four improvements to the Faster R-CNN model and after the ensemble of the four models, R2 of “open” reaches 0.8765, R2 of “close” reaches 0.8539, R2 of “all” reaches 0.8481, higher than the prediction result of either model alone, and can completely replace the manual counting method. We can use this model to quickly extract the dehiscence rate of cotton anther under high temperature (HT) condition. In addition, the percentage of dehiscent anther of randomly selected 30 cotton varieties were observed from cotton population under normal conditions and HT conditions through the ensemble of Faster R-CNN model and manual observation. The result showed HT varying decreased the percentage of dehiscent anther in different cotton lines, consistent with the manual method. ConclusionsThe deep learning technology first time been applied to cotton anther dehiscence status recognition instead of manual method to quickly screen the HT tolerant cotton varieties and can help to explore the key genetic improvement genes in the future, promote cotton breeding and improvement.

Haken conference interpreters in Japan: Exploring status through the sociology of work and of professions

This article investigates the professional status of conference interpreters in Japan, by focusing on interpreters employed as haken, that is, dispatched temporary workers. Combining the perspectives of interpreting studies and the sociology of work, it addresses both internal and external factors upholding interpreters’ status: expertise, autonomy, and authority, on one hand, and social and market dynamics, on the other hand. It provides a thick empirical analysis of status-related factors by drawing on fieldwork data in Japan, including 46 interviews with interpreters and 7 interviews with agency managers. The findings show that internal and external factors intertwine in limiting or upholding interpreters’ status recognition. Despite their expertise and qualifications, conference interpreters in Japan have limited control over their work, because of clients’ expectations of subordination. Furthermore, the monopoly of agencies in the Japanese market constrains their professional visibility. Last but not least, interpreters’ employment as temporary workers and the disproportionate feminisation of the category contribute to societal perceptions of interpreting as an insecure and unrewarding occupation. The findings bear practical implications for the advocacy of interpreters’ status and its betterment in Japan.

Enzootic Bovine Leukosis in Italy: Epidemiological Issues after Free Status Recognition and Measures Applied to Tackle the Last Persistent Clusters

Enzootic Bovine Leukosis (EBL), caused by the bovine leukemia virus (BLV), has been eradicated in over 20 countries, most of which are in Western Europe. The European Commission, in 2017, declared Italy to be an officially EBL-free country by means of Commission Implementing Decision (EU) 2017/1910, despite the presence of some infection clusters located in four regions of Central-Southern Italy. As a consequence of persisting infection, the Italian Ministry of Health established specific eradication measures in these areas. In collaboration with the National Reference Laboratory for the Study of Ruminant Retroviral Infectious Diseases, the Ministry of Health employed data from the veterinary information system digital platform, combined with a gap analysis exercise, to monitor and verify the progress of control activities within infection clusters during the period 2018–2021. Our aim was to identify any remaining gaps and, consequently, specific measures to eliminate the factors favouring EBL persistence, on the basis of a description and analysis of the current data regarding epidemiological trends in Italian clusters. The final goal is to achieve the implementation of a less expensive surveillance plan in these areas, as well. The results of comprehensive analysis showed that the eradication activities had been effectively implemented by official local veterinary services, resulting in a drastic reduction of EBL outbreaks in most territories during the period 2018–2021.

Recognition

This chapter explores recognition, or what social scientists sometimes label status. Recognition is a fundamental need, but it takes different forms. Some people crave respect from peers; others want the applause of multitudes. The quest for recognition can have positive consequences in driving performance and encouraging generosity. But it can easily turn toxic, because acclaim is addictive and the desire can never be fully satisfied. For many people, the more recognition they receive, the more they require -- and fame is often fleeting. Overt self-aggrandizement is usually self-defeating. Mental health and satisfaction is lower among people strongly invested in acclaim than in intrinsic goals such as relationships, personal growth, and contributing to a larger cause or community. Although our craving for recognition is deeply rooted, our culture can do more to refocus what people seek recognition for and to reward those who serve socially valued ends beyond their own.

Research on Driver Status Recognition System of Intelligent Vehicle Terminal Based on Deep Learning

Automobile safety driving technology is a hot topic in today’s society, which is very significant to the social transportation system. Vehicle driving behavior monitoring is the foundation and core of safe driving techniques. The research on existing vehicle safety technology can not only improve the understanding of current safe driving research progress, but also provide reference for future researchers. This paper proposes a state recognition system based on a three-dimensional convolutional neural network, which can identify several improper states frequently encountered by drivers during driving, including drinking, making phone calls, and smoking, and can also issue alarm interventions. The system takes the collected continuous video frame information as the input of the three-dimensional convolutional network, carries out multi-level feature extraction and spatio-temporal information fusion, and identifies the driver state according to the extracted spatio-temporal features. The state is judged by the facial feature points of the video stream, and the design of the video surveillance driver state recognition system is completed. Then, the driver status recognition is improved and optimized, and finally, the actual deployment of the driver status recognition system on the mobile terminal is completed. A large number of experimental results show that the driver status recognition system proposed in this paper has achieved upper identification accuracy.

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.