Pushing for Greater Protection: The Trend toward Greater Protection of Intellectual Property in the Chinese Software Industry and the Implications for Rule of Law in China

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Sensor Node Easy Moving Monitoring Region Location Algorithm in Internet of Things

Because of the influence from geographical location, weather and other kinds of circumstances in monitored areas, the shift of the node location and non-uniform distribution, this paper proposed an improved DV-Hop location algorithm. First of all, the package structure by changing the anchor nodes to reduce the number of hops data acquisition phase node data storage; introducing weights to the average hop distance calculation phase the original average hop distance calculation method was improved, and between the node and anchor node distance calculated on the basis of reference anchor nodes are different; then, iterative refinement of node localization stage through the use of multilateral measurement method and Taylor series. Finally, simulation experiment of this method, and compared with the existing methods, the results prove that the method in this paper can greatly reduce positioning errors without adding hardware equipment and network traffic, improve the positioning accuracy, a better solution to the problem of node localization networking monitoring area

Prototype-guided Cross-task Knowledge Distillation for Large-scale Models

Recently, large-scale pre-trained models have shown their advantages in many tasks. However, due to the huge computational complexity and storage requirements, it is challenging to apply the large-scale model to real scenes. A common solution is knowledge distillation which regards the large-scale model as a teacher model and helps to train a small student model to obtain a competitive performance. Cross-task Knowledge distillation expands the application scenarios of the large-scale pre-trained model. Existing knowledge distillation works focus on directly mimicking the final prediction or the intermediate layers of the teacher model, which represent the global-level characteristics and are task-specific. To alleviate the constraint of different label spaces, capturing invariant intrinsic local object characteristics (such as the shape characteristics of the leg and tail of the cattle and horse) plays a key role. Considering the complexity and variability of real scene tasks, we propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach to transfer the intrinsic local-level object knowledge of a large-scale teacher network to various task scenarios. First, to better transfer the generalized knowledge in the teacher model in cross-task scenarios, we propose a prototype learning module to learn from the essential feature representation of objects in the teacher model. Secondly, for diverse downstream tasks, we propose a task-adaptive feature augmentation module to enhance the features of the student model with the learned generalization prototype features and guide the training of the student model to improve its generalization ability. The experimental results on various visual tasks demonstrate the effectiveness of our approach for large-scale model cross-task knowledge distillation scenes

Intensified Restructuring of the Global Industrial System and Remodeling the Competitive Advantage of Nations

The global financial crisis of 2008 has profoundly changed global economic patterns. In response to the profound changes in the global economic environment and in order to position themselves for future economic development, all countries have engaged in significant industrial restructuring. This has led to a new round of industrial adjustment, transfer and upgrading, as well as profound adjustments in the modes of economic growth. It has also changed the pattern of international competition to some extent. Emerging economies in particular have exhibited “eye-catching” performance in overcoming the crisis, with robust economic recoveries and apparent enhancements of national competitiveness. In the current and forthcoming periods, global industrial upgrading will need more extensive technological support. Technical innovation is a key factor in enhancing the competitive advantage of nations. It will become an important driver for new and innovation modes of industrial development and global industrial benefit adjustments. All countries of the world should make timely adjustments to their economic structures, expedite innovations in industrial technology, and enhance core competencies so as to increase their international competitiveness

Mental health of Chinese people during the COVID-19 pandemic: associations with infection severity of region of residence and filial piety

This study aims to investigate mental health among Chinese people living in areas with differing levels of infection severity during the COVID-19 outbreak. It also assesses the association between reciprocal and authoritarian filial piety and mental health in times of crises. A sample of 1,201 Chinese participants was surveyed between April and June 2020. Wuhan city (where 23.4% of participants resided), Hubei province outside Wuhan (13.4% of participants), and elsewhere in China (63.1% of participants) were categorized into high, moderate, and low infection severity areas, respectively. The Depression, Anxiety, and Stress Scale’s severity cut-points were used to categorize participants. In the overall sample, 20.9, 34.2, and 29.0% of the participants showed elevated (mild to extremely severe) levels of stress, anxiety, and depression. Those in the highest infection severity group were significantly more likely to be categorized as having elevated levels of stress, anxiety, and depression. General linear modeling was performed on a composite mental distress variable (taking into account stress, anxiety, and depression scores). This model indicated that, even after adjusting for group differences in age, gender, education, and filial piety, the high infection severity group displayed more mental distress than the low infection severity groups. The model also found reciprocal filial piety to have a negative association with mental distress. Conversely, authoritarian filial piety was found to be unrelated to mental distress when controlling for the other variables in the model. No evidence was found for an interaction between either authoritarian or reciprocal filial piety and infection severity, which suggests that the negative association observed between reciprocal filial piety and mental distress was relatively consistent across the three infection severity groups. The findings suggest that future public health programs may integrate the promotion of filial piety as a strategy to help Chinese people maintain good mental health in the face of pandemic crises