Development of financial technologies as a driver of changes in the requirements for staffing the financial market

2019 ◽  
pp. 168-180
Author(s):  
I.Ya. Lukasevich ◽  
S.R. Dreving
Keyword(s):  
Financial Market ◽  
Soft Skills ◽  
Digital Technologies ◽  
Professional Competencies ◽  
Educational Standards ◽  
Effective Training ◽  
Practical Recommendations

In this study, the problems of training for the financial market in the conditions of rapidly developing digital technologies are considered. Based on the analysis of trends in the implementation of digitalization processes in various sectors of the financial market, the conclusion is made about the need for the formation of current and future specialists of relevant competencies and skills. It is shown that in order to solve the problem of effective training and retraining of personnel for the financial market, professional and educational standards should be supplemented with digital and general professional competencies (soft skills). Practical recommendations on amendments and additions to professional and educational standards are offered.

scholarly journals Views about sensory modulation from people with schizophrenia and treating staff: A multisite qualitative study

2021 ◽  
pp. 030802262098847
Author(s):  
Tawanda Machingura ◽  
Chris Lloyd ◽  
Karen Murphy ◽  
Sarah Goulder ◽  
David Shum ◽  
...  
Keyword(s):  
Content Analysis ◽  
Treatment Option ◽  
Perceived Risk ◽  
Quantitative Methods ◽  
Qualitative Content Analysis ◽  
Treatment Options ◽  
Occupational Therapists ◽  
Sensory Modulation ◽  
Structured Interviews ◽  
Effective Training

Introduction Current non-pharmacological treatment options for people with schizophrenia are limited. There is, however, emerging evidence that sensory modulation can be beneficial for this population. This study aimed to gain insight into sensory modulation from the user’s and the treating staff’s perspectives. Method A qualitative content analysis design was used. Transcripts from occupational therapists ( n=11) and patients with schizophrenia ( n=13) derived from in-depth semi-structured interviews were analysed for themes using content analysis. Results Five themes emerged from this study: Service user education on the sensory approach is the key; A variety of tools should be tried; Sensory modulation provides a valued treatment option; There are challenges of managing perceived risk at an organisational level; and There is a shortage of accessible and effective training. Conclusion People with schizophrenia and treating staff had congruent perceptions regarding the use of sensory modulation as a treatment option. The findings suggest that sensory modulation can be a valued addition to treatment options for people with schizophrenia. We suggest further research on sensory modulation intervention effectiveness using quantitative methods so these results can be further explored.

scholarly journals Rethinking the Random Cropping Data Augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector

2020 ◽  
Vol 13 (1) ◽  
pp. 34
Author(s):  
Rong Yang ◽  
Robert Wang ◽  
Yunkai Deng ◽  
Xiaoxue Jia ◽  
Heng Zhang
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Back Propagation ◽  
Detection Performance ◽  
Training Data ◽  
Sar Image ◽  
Optical Images ◽  
The Neural Network ◽  
Effective Training ◽  
Standard Configuration

The random cropping data augmentation method is widely used to train convolutional neural network (CNN)-based target detectors to detect targets in optical images (e.g., COCO datasets). It can expand the scale of the dataset dozens of times while consuming only a small amount of calculations when training the neural network detector. In addition, random cropping can also greatly enhance the spatial robustness of the model, because it can make the same target appear in different positions of the sample image. Nowadays, random cropping and random flipping have become the standard configuration for those tasks with limited training data, which makes it natural to introduce them into the training of CNN-based synthetic aperture radar (SAR) image ship detectors. However, in this paper, we show that the introduction of traditional random cropping methods directly in the training of the CNN-based SAR image ship detector may generate a lot of noise in the gradient during back propagation, which hurts the detection performance. In order to eliminate the noise in the training gradient, a simple and effective training method based on feature map mask is proposed. Experiments prove that the proposed method can effectively eliminate the gradient noise introduced by random cropping and significantly improve the detection performance under a variety of evaluation indicators without increasing inference cost.

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