API as a way to interact with social networks

In the realities of the pandemic, chat bots have become indispensable helpers. They do not need a lot of resources and constant human control. A method of interacting with social networks through a specialized software interface Web API, which is the basis of the REST architecture, is considered. The basic structure of requests for receiving and sending data to servers is presented. On the example of the implementation of a chatbot for vk.com, capable of automating a dialogue with users, the main design stages are presented, including the requirements for the implementation and operation mode based on the client-server architecture, implementation and testing. The project server is implemented on a Raspberry Pi4 single-board computer. Demonstrated code for performing basic queries and implemented a Long Polling approach to continuously track and distribute user messages. Methods were formed to obtain the necessary resources from the server, to declare a new resource on the server, to update information on the server, and to delete certain objects from the database. The result was a patented software product "Intelligent assistant of VSUIT for social networks".

Assessment of Smart Home Assistants as an IoT

A smart home personal assistant technology is an intrinsic system, which incorporates many elements such as users, Smart Home Personal Assistants (SPA) devices, cloud, skill provider and other responsive devices. Even though Smart Home Personal Assistants give a robust security and privacy options, the devices face many weaknesses, which make the system vulnerable and can be comprised by adversaries, who can capitalize on limitations to gain access to delicate information and privacy of users. In this research, the aim is to assess how invention and innovation of security and SPA can be harnessed by users to interrelate with the system. Subsequently, this write-up will address both the problems related to the system and attempt to bring in solutions, which makes the technology more adaptable and versatile to all users. Initial studies show that some of the weakness underlying the technology include the open-nature of the voice channel, complexity of the architecture, software implications, and the utility of the technology to less proficient users. As a result, the study anticipates at solving the voice squatting attack, using the SPA intelligent assistant, incorporating a filer to filter the ultrasonic attack and noise as well as trying to assess the efficacy of the elements developed against the voice squatting attacks. The study found out that there is a need to mitigate the attacks on the blockchain technology and Natural Language Processors (NLP) to assure protection of SPA from attacks.

PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture

Background and objective: Arrhythmia is a widely seen cardiologic ailment worldwide, and is diagnosed using electrocardiogram (ECG) signals. The ECG signals can be translated manually by human experts, but can also be scheduled to be carried out automatically by some agents. To easily diagnose arrhythmia, an intelligent assistant can be used. Machine learning-based automatic arrhythmia detection models have been proposed to create an intelligent assistant. Materials and Methods: In this work, we have used an ECG dataset. This dataset contains 1000 ECG signals with 17 categories. A new hand-modeled learning network is developed on this dataset, and this model uses a 3D shape (prismatoid) to create textural features. Moreover, a tunable Q wavelet transform with low oscillatory parameters and a statistical feature extractor has been applied to extract features at both low and high levels. The suggested prismatoid pattern and statistical feature extractor create features from 53 sub-bands. A neighborhood component analysis has been used to choose the most discriminative features. Two classifiers, k nearest neighbor (kNN) and support vector machine (SVM), were used to classify the selected top features with 10-fold cross-validation. Results: The calculated best accuracy rate of the proposed model is equal to 97.30% using the SVM classifier. Conclusion: The computed results clearly indicate the success of the proposed prismatoid pattern-based model.

SMART POWER WHEELCHAIR: PROBLEMS AND CHALLENGES OF PRODUCT APPROACH

This paper focuses on intelligent assistant for power wheelchair (PW) usage in home conditions. Especially in the context of PW intelligent assistant as a consumer product. The main problematic aspects and challenges of smart PW in real application are noted. The approach to formation of system requirements and their classification is offered. The research results proposed and implemented in the ongoing Mobilis project for smart PW. Further prospects of research and development are noted. Also, it is stated that the implementation of smart PW technology opens possibilities to effective integration with new control methods (including brain-computer interfaces).

Research on SPDTRS-PNN Based Intelligent Assistant Diagnosis for Breast Cancer

Background:Breast cancer is the second dangerous cancer in the world. How to identify breast cancer quickly and accurately is of great help to the treatment of breast cancer. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time-consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. Methods:This paper proposes the single-parameter decision-theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We structure fifteen models by combining five dimensionality reduction algorithms with three classification algorithms. We compared the accuracy and test time of fifteen models under different parameters or dimensions. We find that when the parameter value of SPDTRS is 2.5, the classification effect of SPDTRS combined with PNN is better. At this point, the number of 30 attributes of the original breast cancer data dropped to 12. Then the SPDTRS-PNN model is further optimized. We compared the accuracy and test time of the model under different SPREAD values in PNN, and established a better SPDTRS-PNN model.Result:We find the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093s.Conclusion:The experimental results show that the SPDTRS-PNN model can improve the accuracy of breast cancer recognition, reduce the time required for diagnosis, and achieve rapid and accurate breast cancer diagnosis.

Modern Look at Uncertainty in Conceptual Geological Modelling. Development of the Decision Support System for Petroleum Exploration

At the early stages of field life, the subsurface project team operates under lack of information. Due to the high uncertainties, decisions at the exploration and appraisal stages are often influenced by cognitive distortion that leads to overestimation or underestimation of hydrocarbon reserves and, as a result, to suboptimal investment decisions. World practice allows us to identify the most common causes of cognitive bias: the team focus on the most provable according to their view scenario and may ignore data that contradicts the chosen scenario,the opinions of the team members differ in the choice of the most likely scenario,the team members work with geological and geophysical (G&G) data performing separate tasks and may miss important connections between various sources of information. The consequences of these cognitive distortions cause an increase in risk capital, the duration of exploration activities, and the choice of suboptimal field developmentstrategy resulting in a decrease in the effectiveness of the exploration program and the project as a whole. To reduce such risks, it is possible to attract subject matter experts with extensive experience to support the project team. But the amount of experts is limited and this approach cannot be implemented for the entire portfolio of exploration projects. As result of a research project of Gazpromneft in a partnership with IBM Research, an innovative approach was developed for the objective integration of geological and geophysical data. The main idea of this approach is to support the geologist's decisions by an intelligent assistant working on the principles of the modern theory of knowledge engineering. Using the generalized expert knowledge, the intelligent assistant impartially integrates disparate geological information into a set of conceptual geological models (scenarios, objectively evaluates their probabilities, and helps to plan optimal exploration/appraisal activities.