Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation

Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.

A federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean

Learning based credit prediction has attracted great interest from academia and industry. Different institutions hold a certain amount of credit data with limited users to build model. An institution has the requirement to obtain data from other institutions for improving model performance. However, due to the privacy protection and subject to legal restrictions, they encounter difficulties in data exchange. This affects the performance of credit prediction. In order to solve the above problem, this paper proposes a federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean, which can aggregate parameters of each institution via joint training while protecting the data privacy of each institution. Moreover, in actual production and life, there are usually more unlabeled credit data than labeled ones, and the distribution of their feature space presents multiple data-dense divisions. To deal with these, local meanNet model is proposed with a multi-layer label mean based semi-supervised deep learning network. In addition, this paper introduces a cost-sensitive loss function in the supervised part of the local mean model. Conducted on two public credit datasets, experimental results show that our proposed federated learning based approach has achieved promising credit prediction performance in terms of Accuracy and F1 measures. At the same time, the framework design mode that splits data aggregation and keys uniformly can improve the security of data privacy and enhance the flexibility of model training.

Development Training of Android-Based Interactive Learning Application as Supporting Media in Online Learning

<span class="ShortAbstract"><em><span>In supporting the success of online learning, it is necessary to increase the creativity of educators in developing and using learning media applications. The purpose of this research is to provide training and assistance to lecturers, teachers and prospective teacher students in making Windows and Android-based learning applications. The structured training program is carried out in collaboration between lecturers from the Teacher Training and Education Faculty and partner teams from the Permata Bunda Educational Institution through the Kedaireka Matching Fund program. The training was carried out in a hybrid manner with details of 10 participants participating offline and 60 participants participating online. The training was carried out for one day by dividing into 3 types of activities, namely the introduction of learning applications, direct practice of making learning applications and as a closing activity a questionnaire was given as an evaluation of the success of the activity. Based on the evaluation results obtained information that 14.26% of participants have been able to develop learning applications up to the publication stage, 71.43% of participants have been able to develop learning application content and 10% of participants are able to complete the learning media framework design stage, while 4 ,26% are still at the stage of program installation. In addition, at the end of the activity, participants also filled out a satisfaction questionnaire with the ongoing training activities. Based on the response analysis of participants' satisfaction above 85% which is included in the very good category. This shows that participants are very satisfied with the training they have received</span></em></span>

Framework design for competitive marketing mechanism in Iraqi social media

Several organizations in Iraq manufacture similar commodities in this aggressive social trading. The objective of these organizations is diffusing information about their commodities publicly for popularity of the commodities in social media. More returns result in popular commodities and vice versa. The development of a framework incorporating two organizations engaging to broaden the information to the large media has been undertaken. The organizations first identified their initial seed points concurrently and then data was scattered as per the Independent Cascade Model (ICM). The major objective of the organizations is the identification of seed points for the diffusion of data to several points in social media. Significant is also how fast data diffusion can be done. Data effect will arise from either none, one or more nodes in a social interconnection. Evaluation is also accomplished on the number of fraction parts in various sections are affected by the different rates of data diffusion. The simulation result for suggested framework presented better outcomes result for random network 1 and random network 2 comparing with regular network. This framework is used a Hotellingframwork of competition.

Cybersecurity of Robotic Systems: Leading Challenges and Robotic System Design Methodology

Recent years have seen a rapid development of the Internet of Things (IoT) and the growth of autonomous robotic applications which are using network communications. Accordingly, an increasing advancement of intelligent devices with wireless sensors (that means autonomous robotic platforms) operating in challenging environments makes robots a tangible reality in the near future. Unfortunately, as a result of technical development, security problems emerge, especially when considering human–robot collaboration. Two abnormalities often compromise the basic security of collaborative robotic fleets: (a) Information faults and (b) system failures. This paper attempts to describe the methodology of a control framework design for secure robotic systems aided by the Internet of Things. The suggested concept represents a control system structure using blocks as the components. The structure is designed for the robots expected to interact with humans safely and act connected by communication channels. The properties of the components and relations between them are briefly described. The novelty of the proposed concept concerns the security mechanisms. The paper also categorizes two different modes of network attacks summarizing their causal effects on the human–robot collaboration systems. The issue of standardization is also raised. In particular, the works of the National Institute of Standards and Technology (NIST) and European Parliament (EP) on the security templates for communication channels are commented.

Research on Application of Computer Recognition Technology in C Language Programming Modeling System

C language programming is more and more favoured by the majority of technical personnel in embedded systems. The application of C language technology in computer software programming can effectively avoid unnecessary language logic problems, ensure the smooth progress of programming work and effectively improve the quality and efficiency of programming. For the development of C language embedded system, the programming ideas of system software are explained, the functional module division based on hierarchical design is given, and the realization methods of project organization, program framework design, module reuse design, etc. in the software development process are clarified. To solve the contradiction between C language flexibility and application development engineering. Although it is introduced for the ARM platform, the basic experience and algorithms are also suitable for software design on other embedded platforms.