Reviewerly/exHarmony huggingface dataset:
Mobile Image Communications Using Deep Neural Networks for Heterogeneous User Devices in MIMO Channels

This article considers a mobile multimedia communication scenario in which two types of heterogeneous users with different numbers of antennas and different display resolutions are served within the same service area. We propose an efficient image transmission scheme that makes use of deep neural network-based super-resolution techniques, spatial diversity, and diversity-multiplexing tradeoff (DMT) achieving codes. The proposed scheme transmits a low-resolution (LR) image to two types of users, along with residual pixel-error map that contains high-frequency details of high-resolution (HR) images and is produced by a convex optimization technique. Then, a user retaining an HR screen employs super-resolution to reconstruct an HR image from the received LR image, and also exploits the residual map to further enhance the image quality. Our scheme properly trains deep neural network models for super-resolution, based on the source coding rates of the images to be transmitted over bandwidth-limited multiple-input multiple-output (MIMO) channels. That is, various quantization errors resulting from image compression are considered when training the deep neural network-based super-resolution models. For MIMO systems, considering and utilizing the relationship between the number of antennas and the screen resolution, which is based on hardware space of user devices, the proposed scheme encodes an LR image with spatial diversity techniques, and encodes a residual pixel-error map with DMT-achieving codes. Numerical evaluation shows that our scheme significantly outperforms the strategy that broadcasts either HR or LR images to two types of heterogeneous users.
Design and Implementation of an Autonomous Wheelchair

There are millions of disabled people in the world being unable to perform regular activities due to the incompetence of their movability. Many of them lie on the floor and many more die due to different obscurities that could have been foreclosed. Mobility can be achieved by the use of conventional wheelchairs but they can't provide complete mobility since they require the constant presence of attendee. They can play an important role in society if they can get mobile by themselves. It can be achieved through the use of autonomous wheelchairs. In this paper, such an autonomous wheelchair is designed and implemented that eradicates the constant presence of attendee. Four control systems are installed at the wheelchair as gesture control system, voice control system, eye control system and joystick control system that provide the provision to select any one of the control systems based on the disability of the patient.
Object Detection and Counting Using Unsupervised Method

Differentiating and finding similar patterns of images from a video frame source emerges as the basic elemental task in image processing. In this paper, various algorithms on object identification, shape identification, color identification, object counting are analyzed and compared to obtain new unsupervised method. These methods find applications in the field of defense, security check, healthcare and Traffic monitoring. Various challenging features and applications of object Detection, identification and counting methods are elaborated with new algorithm with least set of dataset. In addition, the different steps involved in object detection and results for several steps are discussed. Moreover, the proposed algorithmic method is able to process the unstructured and structured images in multiple visual concepts. Further, these algorithms can be applied in wide area and crowded scenes with high precision identification.
Basics of creating an applied intelligent system for diagnostic and management of illness cognition of patients with neurological pathology

Abstract The article is devoted to the creation of an applied intelligent system used for diagnosis and management of illness cognition of neurological pathology patients (IS DIMIC). The applied IS DIMIC is constructed based on the intelligent instrumental software IMSLOG using matrix method of data and knowledge representation, test methods for pattern recognition, fault-tolerant irredundant unconditional diagnostic tests and fault-tolerant mixed diagnostic tests. The application of applied IS DIMIC will allow to reveal various kinds of regularities of the patients’ illness cognition on the basis of features that determine the peculiarities of basic constructs reflecting the process of cognitive overestimation of the illness consequences. The applied IS DIMIC will allow to make diagnostic decisions and justify them using graphic tools, including cognitive ones, which is very important for patient rehabilitation management.
AMRnet: A Real-Time Automatic Modulation Recognition Network for Wireless Communication System

In recent years, deep learning has brought new opportunities and challenges to the wireless communication field, owing to its expressive capacity and convenient optimization capability. In this paper, we propose a real-time Automatic Modulation Recognition Network (AMRnet) for signal transmission process, which can be easily applied to mobile and embedded wireless communication applications (e.g., The GNU Radio system based on USRP platform). Our proposed AMRnet consists of a series of convolutional neural networks based on the design theory of lightweight networks. More specifically, depthwise separable convolution structure is used to reduce computational complexity of the whole network. Residual block is included to improve the robustness for the simple backbone network. In addition, channel attention mechanism is added to lay more emphasis on the channel-wise information due to the unique data futures of the I/Q signal. We present numerous experiments on resource and accuracy tradeoffs and show strong performance compared to a number of state-of-the-art methods. This work will have a great impact on the future communication systems.
A hybrid Classifier using Weighted ELM with Biogeography Based Optimization for Hepatitis Diagnosis

Hepatitis is a liver disease caused by viral infections. An early diagnosis of Hepatitis is necessary to avoid mortality rate. In this research, the hybrid approach Weighted Extreme Learning Machine with Biogeography Based Optimization (WELM-BBO) is designed to diagnose Hepatitis. The machine learning model Weighted ELM has good generalization and efficient learning capacity. For imbalanced dataset, the classification algorithms always produce less accuracy. Since, WELM assigns distinct weights to minority and majority classes. The random initialization of the input parameters in WELM such as input weight and bias produce inconsistent and inaccurate classification results and degrade the classification performance. Hence, the Weighted ELM algorithm is hybridized with Biogeography Based Optimization (BBO) to find efficiently the optimum parameters such as input weight and bias. The proposed system is measured over hepatitis dataset. The classification efficiency of the proposed model is analyzed using the statistical parameters sensitivity, specificity, accuracy, and G-mean. The computational result clearly proved that WELM-BBO method yielded an accuracy of 86.96%, which is superior to other methods such as WELM, ELM, IBO-WELM, ACO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> -WELM, BAT-ELM.
Primitive Action Segmentation and Encoding during Gesture based Demonstration

Traditional Learning from Demonstration (LfD) approaches are mainly based on robot motion and focuses on how to processing and representing the trajectories. These low level models closely related to specific task and cannot be reasoned and reused in future tasks. To solve this problem, it is necessary to segment the robot motions and associate segments with semantics, which are not trivial tasks. In this paper, gesture information is considered in the learning process. Gesture plays a key role in our daily communication. It provides a noncontact, natural and easy-to-use way for operating a robot. It contains rich information. The hand gestures both dynamic and static can be mapped to discrete commands and the hand position/orientation/movement can also be converted to continuous robot motions. However, in traditional LfD methods, the latent information during the demonstration is ignored. In order to align the recorded demonstrations and divide them into sub-skills with semantics, the key-frame that connect two adjacent sub-skills has to be identified first. Traditional alignment methods are based on curvature change of robot motions, which may lead to inaccurate segmentation. The paper uses hand gesture to assist the classification process, which can significantly reduce the distortion of data and improve the accuracy of segmentation. Furthermore, the key-frames and sub-skills are used to construct a GMM-HMM model, which can automatically generate a structured representation of the task model. A pick-and-place task is used to verify the effectiveness of the proposed method. The results show that the dataset is properly aligned and the identified sub-skill can be recognized by the skill encoding model.
Key aspects of the implementation of an automated system module for assessing the quality of academic software products

The article discusses the main problems of training specialists in the IT field, in particular programmers. Problems arising in the process of teaching programming do not allow to implement the learning process and its modernization. The proposed solution is a software package that, firstly, solves existing problems in the educational process, and secondly, has unique capabilities regarding alternative solutions. Briefly describes the dynamic testing module, which is one of the key in the software package. Since the modern approach to the education of IT specialists involves the intensification of the educational process, the introduction of modern pedagogical tools is required, which will improve the quality of the educational process and, as a result, meet the required educational competencies in the field of information technologies.
PCA likelihood ratio test approach for attributed social networks monitoring

One of the most important factors in building and changing communication mechanisms in social networks is considering features of the members of social networks. Most of the existing methods in network monitoring don’t consider effects of features in network formation mechanisms and others don’t lead to reliable results when the features abound or when there are correlations among them. In this article, we combined two methods principal component analysis (PCA) and likelihood method to monitor the underlying network model when the features of individuals abound and when some of them have high correlations with each other.
A Deep Neural Network Object Detection Method Using Multiscale Poisson Fusion

The deep neural network method has been widely used in the field of video object detection. However, the detection performance of such detectors are very sensitive to scaling change of targets. To improve detecting performance, it's necessary to use a large number of sample data at different scales to train the model, but generally we could not get real multi-scale data of one scene. Aiming at Faster-RCNN, a state-of-the-art object detector, this paper proposes a deep neural network object detection method based on multi-scale Poisson fusion, its augment samples using Poisson method, which extracts sample objects from the original video frames and uses the Laplacian differential operator to calculate the divergence of sample objects according to the divergence of target area. After multi scaling at the sample objects, the Poisson equation is used to merge the scaling objects into the background at the target area, thus generating a large number of sample data at different scales. The experiment proves that the multi-scale sample data which generated by Possion fusion method can effectively perform the fine-tuning training for Faster-RCNN and improve its performance when detecting objects at different scales.
Performance Optimization of Wildlife Recognition with Distributed Learning in Ecological Surveillance

Recently, deep learning technology has been widely used in ecological environment monitoring, but there are still huge challenges in specific tasks. For example, data sets have increased over the past few years, and in a CPU environment, training is quite time consuming. In order to solve these problems, we bring up a novel and effective method to improve training speed. In this paper, we make use of multi-GPUs to achieve distributed parallel acceleration. Small and shallow networks are not suitable for distributed training, because the computation of each parameter in this network is much higher than that of multi-layer perception or automatic encoder architecture, we use convolutional neural network with parameter sharing as the training network. In this paper, the main adopted method is to compare the performance of training wildlife recognition in single GPU and multi-GPUs environments by using distributed deep learning framework TensorFlow. The experimental results show that multi-GPUs which adopt distributed architecture can significantly accelerate training time consumption than single GPU. The results of this experiment also provides strong support for our follow-up work.
A mixed-methods study exploring the effect of augmented learning for paramedic students in ECG training

The purpose of this study was to investigate the effects of augmented reality (AR) technology on the achievements, self-efficacy beliefs and views of paramedic students in electrocardiography education. In the study, mixed methods including quantitative and qualitative methods were used. The sample of the study included a total of 71 first-year paramedic students studying at a university in Turkey. An academic achievement test, self-efficacy scale, and semi-structured interview form were used as data collection tools. As a result of the study, it was found that AR had a significant medium effect on students’ academic achievements but did not have any effect on their self-efficacy beliefs. In addition, based on the interviews conducted with the students in the experimental group, it was determined that the students were satisfied with the use of AR in their courses and wanted to use this technology in different courses and subjects in the future.
WS-CDL: Coordinating Ubiquitous Devices in Pervasive Environments Using a Web Standard

We are permanently interacting with ubiquitous devices in our daily lives, and with services they present. Ubiquitous devices need to cooperate with other devices in order to provide services. There are some cooperation mechanisms for provide services, but they are commonly proprietary. The few proposals from academy have hardly achieved an impact in practice. This it is not in harmony with the situation of the Internet environment and web services, which have standardized mechanisms for the composition of services. To applicate Service Oriented Architecture (SOA) specifications and standards for ubiquitous devices coordination in pervasive environments. We apply an adaptation of Design Science in our environment to allow the iterative construction and evaluation of prototypes. We built and put into operation a coordination framework for ubiquitous devices based on WS-CDL, along with a proof of concept. In addition, we contribute to WS-CDL language in order to support the characteristics of specific ubiquitous devices.
On the Detection of Video's Ethnic Vietnamese Thai Dance Movements

The problem addressed in this paper is the one of classifying Vietnamese dances' videos. In particular, we focus on an automatic detection of movements in the Ethnic Vietnamese Thai dances (ETVD). We first propose an ontology-based description of ETVD movements in terms of main movements' steps. We then associate with each movement step a profile containing typical features that characterize a movement step. The automatic detection of ETVD movements is based on a correlation method that matches movements' steps profiles with concepts present in frames of dances' videos. The last part of the paper contain experimental studies that show the good classification rate of our ETVD movement detection method.
Software exception handling mechanism for Critical Situations in Healthcare Domain

For critical health care applications, in which life of human being is at risk, there the role and task of exceptions and its effective handling are not only essential but of supreme importance. To ensure robustness in such applications, we extended our previous work and analyze special exceptional cases in a hospital system. We investigated and built situation-conscious functionalities for few special situations in a hospital and observed that the stretched explicitness of unique case dealing with dependent on our scheme can sustain its feasibility and leads to less failure in such applications.
Inscriptive instruments: emergent sonic technologies from the analog to the digital

In recent years the “sonic” emerged as an important unit of critical analysis in the ever-broadening field of sound studies. Sonicity offers an expansive role for sound by placing it in proximity t...
ЭЛИТАРНЫЕ ОРИЕНТАЦИИ ПРОФЕССИОНАЛЬНОГО САМООПРЕДЕЛЕНИЯ В ОБРАЗОВАТЕЛЬНОЙ СРЕДЕ

&#x0D; &#x0D; &#x0D; The paper deals with topical issues of vocational guidance for young people, specifies innovative forms of vocational orientation in the educational environment. The active character of the subject of professional self-determination is emphasized, the psychological and pedagogical conditions of the formation of such activity are highlighted. Special attention is paid to the active position of the higher education institution and its teachers. The effectiveness of vocational guidance work is summarized in the context of the introduction of scientifically based approaches to professional agitation. Presents examples of campaign activities in the educational environment used in the study.&#x0D; &#x0D; &#x0D;
PRACTICAL ASPECTS OF THE PROBLEM OF TRAINING SPECIALISTS IN THE SOCIAL SPHERE TO INTERPROFESSIONAL INTERACTION

&#x0D; &#x0D; &#x0D; The concept of research is based on the needs of a network of practice bases, based on the use of theories and concepts of psychology, sociology, pedagogy, social work and other humanities, the study of laws and approaches to understanding the content of interprofessional interaction.The content of the research concept is to develop a procedural model of training professionals in the social sphere to interprofessional interaction and implemented on 2 levels: theoretical-methodological and practical, each of which is represented by a set of scientific approaches.The methodological basis of the research is systematic, terminological, socio- systematic, structural-activity and synergetic, sociological, and competent methodological approaches.The result of obtaining future specialists in the social sphere of high-quality vocational education is a set of acquired in the higher educational institution acquired competences in the process of learning: educational-cognitive, interpersonal interaction, communicative, intercultural, socio-cultural and technical (skills).The structure of the content of vocational education, which includes the following components: social, psychological and pedagogical, methodical, is defined in order to determine the effectiveness of the training of future specialists in the social sphere at a higher educational establishment. Determining the role of interdisciplinary material in the classroom will help to understand the nature of the interaction between the elements of individual disciplines - linear (new knowledge serves to deepen the already acquired); the opposite (new knowledge specifies or directs the assimilated earlier information); problem (new knowledge creates conditions for solving educational problem). Correct building classes will help to understand the nature of content relationships that can be synchronous, reproductive, and promising. In practice, an interdisciplinary approach can be implemented in two main areas: the relationship between different sciences (disciplines), without violating their autonomy, uniqueness, originality; interdisciplinaryism acts as a real tool for the consolidation of sciences (disciplines), the further development of which is crucial for both science and education.The chosen integration methodological approach of the study does not exclude the trilateral structure of the marked interprofessional interaction in the form of a combination of motivational, target and implementation (executive) planes. The result of obtaining future specialists in the social sphere of high-quality professional education is a set of acquired in the process of studying in the institution of higher education competencies. This is evidenced, in particular, by the analysis of professionally important competences that, according to respondents, should be characteristic of specialists in the social sphereThe results obtained prompted us to investigate, and what, according to experts, is the basis of interprofessional interaction.&#x0D; &#x0D; &#x0D;
РЕЗУЛЬТАТИ ПЕДАГОГІЧНОГО ЕКСПЕРИМЕНТУ З ФОРМУВАННЯ ІНФОРМАЦІЙНОЇ КОМПЕТЕНТНОСТІ МАЙБУТНЬОГО ПЕДАГОГА ПРОФЕСІЙНОГО НАВЧАННЯ В ЗАКЛАДАХ ВИЩОЇ ОСВІТИ

&#x0D; &#x0D; &#x0D; Training teachers is an important task of modern pedagogy and requires new approaches to the organization of the educational process. The use of information and communication technologies for the purpose of forming the information competence of the future vocational teacher is an important element of the formation of his professional competence. The article describes the stages of conducting a pedagogical experiment to develop the information competence of a future teacher of vocational training in domestic higher education institutions. The organization and methodology of the experimental study are described in detail, its results are given, the methods of diagnostics are described and so on.&#x0D; &#x0D; &#x0D;
Construction of Primary and Secondary School Teachers' ICT Application Micro-Competence Framework

The purpose of this study is to construct micro-competence framework and mine the relationship among micro-competences. It provides graph support for the intelligent push of teachers' ICT application competence assessment task. In view of the limitations of the current competence assessment, by analyzing the principles and basis for construction of micro-competence framework. This paper puts forward the ideas and methods of collaborative construction of domain experts and machines. Based on the Standard for Primary and Secondary School Teachers' ICT Application Competence (Trial). The author decomposes teachers' ICT application competence with reference to relevant standards, documents and research results at home and abroad, and determines micro-competence and its weight through expert consultation. Finally, micro-competence framework was constructed, the ideas and methods of mining the relationship among micro-competences using association rules algorithm were putting forward.