Robot creativity: humanlike behaviour in the robot-robot interaction

Artificial Intelligence development is mainly directed toward imitat­ing human reasoning and performing different tasks. For that purpose, related software and program solution where artificial intelligence is used have mostly thinking abilities. However, there are many questions to answer in ongoing AI research, especially when we come to the point which is addressing humanlike behaviour and reasoning triggered by emotions. In this paper, we are presenting an interactive installation Botorikko: Machine Create State, which is part of the Syntropic Counterpoints art/research project. We are exposing AI cyber clones to some of the fundamental questions for humankind and challenge their creativity. The robots are trained by using the publications Machiavelli and Sun Tzu and confronted to the crucial questions related to moral, ethic, strategy, politics, diplo­macy, war etc. We are using a recurrent neural network (RNN) and robot-robot interaction to trigger unsupervised robot creativity and humanlike behaviour on generated machine-made content

Agrometeorological research and its measuring technique.

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A Framework for Controlling Wheelchair Motion by using Gaze Information

Users with severe motor ability are unable to control their wheelchair using standard joystick and hence an alternative control input is preferred. In this paper a method on how to enable the severe impairment user to control a wheelchair via gaze information is proposed. Since when using such an input, the navigation burden for the user is significantly increased, an assistive navigation platform is also proposed to reduce the user burden. Initially, user information is inferred using a camera and a bite-like switch. Then information from the environment is obtained using combination of laser and Kinect sensors. Eventually, both information from the environment and the user is analyzed to decide the final control operation that according to the user intention and safe from collision. Experimental results demonstrate the feasibility of the proposed approach

Variants of the Game of Nim that have Inequalities as Conditions

In this article the authors are going to present several combinatorial games that are variants of the game of Nim. They are very different from the traditional game of Nim, since the coordinates of positions of the game satisfy inequalities. These games have very interesting mathematical structures. For example, the lists of P-positions of some of these variants are subsets of the list of P-positions of the traditional game of Nim. The authors are sure that they were the first people who treated variants of the game of Nim conditioned by inequalities. Some of these games will produce beautiful 3D graphics (indeed, you will see the Sierpinski gasket when you look from a certain view point). We will also present some new results for the chocolate problem, a problem which was studied in a previous paper and related to Nim. The authors make substantial use of Mathematica in their research of combinatorial games

The RNA-Induced Silencing Complex Is a Mg2+-Dependent Endonuclease

AbstractIn the Drosophila and mammalian RNA interference (RNAi) pathways, target RNA destruction is catalyzed by the siRNA-guided, RNA-induced silencing complex (RISC). RISC has been proposed to be an siRNA-directed endonuclease, catalyzing cleavage of a single phosphodiester bond on the RNA target. Although 5′ cleavage products are readily detected for RNAi in vitro, only 3′ cleavage products have been observed in vivo. Proof that RISC acts as an endonuclease requires detection of both 5′ and 3′ cleavage products in a single experimental system. Here, we show that siRNA-programmed RISC generates both 5′ and 3′ cleavage products in vitro; cleavage requires Mg2+, but not Ca2+, and the cleavage product termini suggest a role for Mg2+ in catalysis. Moreover, a single phosphorothioate in place of the scissile phosphate blocks cleavage; the phosphorothioate effect can be rescued by the thiophilic cation Mn2+, but not by Ca2+ or Mg2+. We propose that during catalysis, a Mg2+ ion is bound to the RNA substrate through a nonbridging oxygen of the scissile phosphate. The mechanism of endonucleolytic cleavage is not consistent with the mechanisms of the previously identified RISC nuclease, Tudor-SN. Thus, the RISC-component that mediates endonucleolytic cleavage of the target RNA remains to be identified

Deep transfer learning application for automated ischemic classification in posterior fossa CT images

Abstract—Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis

Non-contact Heart Rate Monitoring Analysis from Various Distances with different Face Regions

Heart rate (HR) is one of vital biomedical signals for medical diagnosis. Previously, conventional camera is proven to be able to detect small changes in the skin due to the cardiac activity and can be used to measure the HR. However, most of the previous systems operate on near distance mode with a single face patch, thus the feasibility of the remote heart rate for various distances remains vague. This paper tackles this issue by analyzing an optimal framework that capable to works under the mentioned issues. Initially, plausible face landmarks are estimated by employing cascaded of regression mechanism. Next, the region of interest (ROI) was constructed from the landmarks in a face location where non rigid motion is minimal. From the ROI, temporal photoplethysmograph (PPG) signal is calculated based on the average green pixels intensity and environmental illumination is separated using Independent Component Analysis (ICA) filter. Eventually, the PPG signal is further processed using series of temporal filter to exclude frequencies outside the range of interest prior to estimate the HR. As a conclusion, the HR can be detected up to 5 meters range with 94% accuracy using lower part of face region

A Hybrid Approach for Counting Templates in Images

© 2020 ACM. In the research, hybrid algorithm for counting repeated objects in the image is proposed. Proposed algorithm consists of two parts. Template matching sub-algorithm is based on normalized cross correlation function which is widely used in image processing application. Template matching can be used to recognize and/or locate specific objects in an image. Neural network sub-algorithm is needed to filter out false positives that may occur during cross correlation function evaluation. In the last section of the paper experimental evaluation is carried out to estimate the performance of the proposed template matching algorithm for images of blood microscopy and chamomile field image. In the first case, the task is to count erythrocytes in the blood sample. In the second case, it is needed to count the flowers in the field. For all 2 datasets we got precise results that coincides with actual number of objects in image. The reason of such performance is that convolutional neural network sub-algorithm improved initial results of template-matching sub-algorithm based on correlation function

Development of Human Fall Detection System using Joint Height, Joint Velocity, and Joint Position from Depth Maps

Human falls are a major health concern in many communities in today’s aging population. There are different approaches used in developing fall detection system such as some sort of wearable, ambient sensor and vision based systems. This paper proposes a vision based human fall detection system using Kinect for Windows. The generated depth stream from the sensor is used in the proposed algorithm to differentiate human fall from other activities based on human Joint height, joint velocity and joint positions. From the experimental results our system was able to achieve an average accuracy of 96.55% with a sensitivity of 100% and specificity of 95